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results/ +# ./debug/ +# *.zip +# .DS_Store +# *.pyc +# .idea/ +# ./install_dependencies.sh +# ./install_sc2.sh + diff --git a/123.py b/123.py new file mode 100644 index 0000000..ed0f110 --- /dev/null +++ b/123.py @@ -0,0 +1 @@ +print('hello') \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..afdfe50 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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The proposed PI and PE model architectures **can be easily plugged into any existing MARL algorithms and boost their performance**. +* (3) :rocket: **The enhanced algorithm achieves State-Of-The-Art (SOTA) performance on SMAC-V1 and SMAC-V2** (without restricting the agent field-of-view and shooting range to a cone). + +``` +[2023-07 update]: Commit the support for SMAC-V2. +``` + +## 1. Model Architecture of Hyper Policy Network (HPN) + +![Agent permutation invariant network with hypernetworks](./doc/figure/API-HPN.png) + +HPN incorporates [hypernetworks](https://arxiv.org/pdf/1609.09106) to generate different +weights ![](http://latex.codecogs.com/svg.latex?W_i)s for different input +components ![](http://latex.codecogs.com/svg.latex?x_i)s to improve representational capacity while ensuring the +same ![](http://latex.codecogs.com/svg.latex?x_i) always be assigned with the same +weight ![](http://latex.codecogs.com/svg.latex?W_i). The architecture of our HPN is shown in the above Figure (b). We +also take the ![](http://latex.codecogs.com/svg.latex?Q_i(o_i)) as an example. The model mainly composes of two modules: + +**Permutation Invariant Input Layer.** [hypernetworks](https://arxiv.org/pdf/1609.09106) are a family of neural +architectures which use one network, known as hypernetwork, to generate the weights for another network. In our setting, +the hypernetwork is utilized to generate a different ![](http://latex.codecogs.com/svg.latex?W_i) for +each ![](http://latex.codecogs.com/svg.latex?x_i) of the input set ![](http://latex.codecogs.com/svg.latex?X_j). As +shown in above Figure (b), ![](http://latex.codecogs.com/svg.latex?X_j) (which can be viewed as a batch +of ![](http://latex.codecogs.com/svg.latex?m) ![](http://latex.codecogs.com/svg.latex?x_i)s each of which is of +dimension ![](http://latex.codecogs.com/svg.latex?k), represented by different shades of blue) is firstly fed into a +shared hypernetwork (marked in yellow), whose input size is ![](http://latex.codecogs.com/svg.latex?k) and output size +is ![](http://latex.codecogs.com/svg.latex?k*h). Then, the corresponding outputs are reshaped +to ![](http://latex.codecogs.com/svg.latex?\[k,h\]) and serve as the submodule +weights ![](http://latex.codecogs.com/svg.latex?W_i)s of the normal FC layer (see Figure (a)). Note that +different ![](http://latex.codecogs.com/svg.latex?x_i)s will generate +different ![](http://latex.codecogs.com/svg.latex?W_i)s and the same ![](http://latex.codecogs.com/svg.latex?x_i) will +always correspond to the same ![](http://latex.codecogs.com/svg.latex?W_i). Then, +each ![](http://latex.codecogs.com/svg.latex?x_i) is multiplied by ![](http://latex.codecogs.com/svg.latex?W_i) and all +multiplication results and the bias ![](http://latex.codecogs.com/svg.latex?b) are summed together to get the output. +Since each element ![](http://latex.codecogs.com/svg.latex?x_i) is processed separately by its +corresponding ![](http://latex.codecogs.com/svg.latex?W_i) and then merged by a permutation invariant 'sum' function, +the permutation invariance is reserved. + +**Permutation Equivariance Output Layer.** Similarly, to keep the whole network permutation equivariance, the submodular +weights and bias of the agent-related actions in the output layer, +e.g., ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{attack}) of SMAC, are also generated by a +hypernetwork. As mentioned above, the input ![](http://latex.codecogs.com/svg.latex?x_i) and +output ![](http://latex.codecogs.com/svg.latex?W_i) of the hypernetwork always correspond one-to-one, so the input order +change will result in the same output order change, thus achieving permutation equivariance. + +We emphasize that HPN is a general design and can be easily integrated into existing MARL algorithms ( +e.g., [VDN](https://arxiv.org/pdf/1706.05296?ref=https://githubhelp.com) +, [QMIX](http://proceedings.mlr.press/v80/rashid18a/rashid18a.pdf) +, [MADDPG](https://proceedings.neurips.cc/paper/2017/file/68a9750337a418a86fe06c1991a1d64c-Paper.pdf) +, [MAPPO](https://arxiv.org/pdf/2103.01955?ref=https://githubhelp.com)) to boost the learning speed as well as the +converged performance. All parameters of HPN are simply trained end-to-end with backpropagation according to the +corresponding RL loss function. + +## 2. Experimental Results on SAMC-V1 + +We mainly evaluate our methods on the challenging StarCraft II micromanagement +benchmark [(SMAC)](https://github.com/oxwhirl/smac). + +**** + +``` +StarCraft 2 version: SC2.4.10. difficulty: 7. +``` + +| Senarios | Difficulty | HPN-QMIX | +|----------------|:----------:|:----------------------------------:| +| 8m_vs_9m | Hard | **100%** | +| 5m_vs_6m | Hard | **100%** | +| 3s_vs_5z | Hard | **100%** | +| bane_vs_bane | Hard | **100%** | +| 2c_vs_64zg | Hard | **100%** | +| corridor | Super Hard | **100%** | +| MMM2 | Super Hard | **100%** | +| 3s5z_vs_3s6z | Super Hard |**100%** | +| 27m_vs_30m | Super Hard | **100%** | +| 6h_vs_8z | Super Hard | **98%** | + +### 2.1 Applying HPN to fine-tuned VDN and QMIX. + +![Applying HPN to fine-tuned VDN and QMIX](./doc/figure/exp_comparison_with_SOTA2.png) + +### 2.2 Applying HPN to QPLEX and MAPPO. + +![Applying HPN to QPLEX](./doc/figure/HPN-QPLEX.png) +![Applying HPN to MAPPO](./doc/figure/HPN-mappo.png) + +### 2.3 Comparison with baselines considering permutation invariance or permutation equivariance. + +![Comparison with Related Baselines](./doc/figure/exp_comparison_with_baselines.png) + +### 2.4 Transfer results. + +Apart from achieving PI and PE, another benefit of HPN is that it can naturally handle variable numbers of inputs and +outputs. Therefore, as also stated in the conclusion section, HPN can be potentially used to design more efficient +multitask learning and transfer learning algorithms. For example, we can directly transfer the learned HPN policy in one +task to new tasks with different numbers of agents and improve the learning efficiency in the new tasks. Transfer +learning results of 5m → 12m, 5m_vs_6m → 8m_vs_10m, 3s_vs_3z → 3s_vs_5z are shown in the following figures. We see that +the previously trained HPN policies can serve as better initialization policies for new tasks. +![Transfer previous policy v.s. learning from scratch](./doc/figure/MA_transfer.png) + +## 3. Experimental Results on SAMC-V2 + +### 3.1 Changes of SMAC-V2. + +SMAC-v2 makes three major changes to SMAC: randomising start positions, randomising unit types, and restricting the +agent field-of-view and shooting range to a cone. These first two changes increase more randomness to challenge +contemporary MARL algorithms. The third change makes features harder to infer and adds the challenge that agents must +actively gather information (require more efficient exploration). **Since our target is not to design more efficient +exploration algorithms, we keep the field-of-view and attack of the agents a full circle as in SMAC-V1.** + +* **Random Start Positions:** Random start positions come in two different types. First, there is the `surrounded` + type, where the allied units are spawned in the middle of the map, and surrounded by enemy units. This challenges the + allied units to overcome the enemies approach from multiple angles at once. Secondly, there are the `reflect_position` + scenarios. These randomly select positions for the allied units, and then reflect their positions in the midpoint of + the map to get the enemy spawn positions. Examples are shown in the figure + below. ![Random Start Positions](./doc/figure/smac_v2_random_start_positions.png) +* **Random Unit Types:** Battles in SMAC-V2 do not always feature units of the same type each time, as they did in + SMAC. Instead, units are spawned randomly according to certain pre-fixed probabilities. Units in StarCraft II are + split up into different races. Units from different races cannot be on the same team. For each of the three races ( + Protoss, Terran, and Zerg), SMAC-V2 uses three unit types. Detailed generation probabilities are shown in the figure + below. ![Random Start Positions](./doc/figure/smac_v2_random_unit_types.png) + +### 3.2 Experimental Results. + +**Our HPN can naturally handle the two types of new challenges.** Thanks to the PI and PE properties, our HPN is more +robust to the randomly changed start positions of the entities. Thanks to the entity-wise modeling and using +hypernetwork to generate a customized `weight matrix` for each type of unit, HPN can handle the randomly generated unit +types as well. The comparisons of HPN-VDN with VDN on three difficult scenarios across the three races (Protoss, Terran, +and Zerg) are shown in the figures below. Results show that our HPN significantly improves the sample efficiency and the +converged test win rates of the baseline VDN. +![HPN-VDN v.s. VDN](./doc/figure/smac_v2_results.png) + +## 4. How to use the code? + +### 4.1 Install this repository. +```shell +# Step-1: Clone the repository and enter the folder. +git clone git@github.com:tjuHaoXiaotian/pymarl3.git +cd pymarl3 + +# Step-2: Install StarCraftII add the custom maps. +chmod +x install_sc2.sh +./install_sc2.sh + +# Step-3: Install PyTorch and other Python packages. +chmod +x install_dependencies.sh +./install_dependencies.sh +``` + +### 4.2 Detailed command lines to reproduce all experimental results (on SMAC-V1). + +```shell +# For SMAC, take the hpn_qmix, qmix, hpn_qplex and qplex over all hard and super-hard scenarios for example. + +# 5m_vs_6m +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# 3s5z_vs_3s6z +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=3s5z_vs_3s6z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=4 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=3s5z_vs_3s6z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=4 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=3s5z_vs_3s6z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=4 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=3s5z_vs_3s6z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=4 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# 6h_vs_8z +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=6h_vs_8z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=500000 batch_size=128 td_lambda=0.3 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=6h_vs_8z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=500000 batch_size=128 td_lambda=0.3 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=6h_vs_8z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=500000 batch_size=128 td_lambda=0.3 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=6h_vs_8z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=500000 batch_size=128 td_lambda=0.3 hpn_head_num=2 + +# 8m_vs_9m +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=8m_vs_9m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=8m_vs_9m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=8m_vs_9m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=8m_vs_9m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# 3s_vs_5z +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=3s_vs_5z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=3s_vs_5z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=3s_vs_5z obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 hpn_head_num=2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=3s_vs_5z obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 hpn_head_num=2 + +# corridor +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=corridor obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=corridor obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=corridor obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=corridor obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# MMM2 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=MMM2 obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=MMM2 obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=MMM2 obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=MMM2 obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# 27m_vs_30m +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=27m_vs_30m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=27m_vs_30m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=27m_vs_30m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=27m_vs_30m obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# 2c_vs_64zg +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=2c_vs_64zg obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=2c_vs_64zg obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=2c_vs_64zg obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=2c_vs_64zg obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +# bane_vs_bane +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qmix --env-config=sc2 with env_args.map_name=bane_vs_bane obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qmix --env-config=sc2 with env_args.map_name=bane_vs_bane obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_qplex --env-config=sc2 with env_args.map_name=bane_vs_bane obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=qplex --env-config=sc2 with env_args.map_name=bane_vs_bane obs_agent_id=True obs_last_action=True runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +``` + +### 4.3 Detailed command lines to reproduce the experimental results (on SMAC-V2). + +```shell +#%%%%%%%%%%%%%%%%%%% sc2_v2_terran %%%%%%%%%%%%%%%%%%%%% +CUDA_VISIBLE_DEVICES="1" python src/main.py --config=hpn_vdn --env-config=sc2_v2_terran with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=vdn --env-config=sc2_v2_terran with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +#%%%%%%%%%%%%%%%%%%% sc2_v2_protoss %%%%%%%%%%%%%%%%%%%%% +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=hpn_vdn --env-config=sc2_v2_protoss with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +CUDA_VISIBLE_DEVICES="1" python src/main.py --config=vdn --env-config=sc2_v2_protoss with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +#%%%%%%%%%%%%%%%%%%% sc2_v2_zerg %%%%%%%%%%%%%%%%%%%%% +CUDA_VISIBLE_DEVICES="1" python src/main.py --config=hpn_vdn --env-config=sc2_v2_zerg with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=vdn --env-config=sc2_v2_zerg with obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 mixer=vdn + +``` + +## 5. Citation + +```text +@article{hao2022api, + title={Boosting Multi-Agent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks}, + author={Hao Xiaotian, Hao Jianye, Mao Hangyu, Wang Weixun, Yang Yaodong, Li Dong, Zheng Yan, Wang Zhen}, + journal={The Eleventh International Conference on Learning Representations.}, + year={2023} +} +``` + + + diff --git a/doc/README(old-version).md b/doc/README(old-version).md new file mode 100644 index 0000000..9588010 --- /dev/null +++ b/doc/README(old-version).md @@ -0,0 +1,129 @@ +# API: Boosting Multi-Agent Reinforcement Learning via Agent-Permutation-Invariant Networks + +Open-source code for [API: Boosting Multi-Agent Reinforcement Learning via Agent-Permutation-Invariant Networks](https://arxiv.org/abs/xxxxx). + +[TOC] + +## 1. Motivation + +### 1.1 Permutation Invariance and Equivariance + + + +**Permutation Invariant Function.** A function ![](http://latex.codecogs.com/svg.latex?f:X\rightarrow\text{Y}) where ![](http://latex.codecogs.com/svg.latex?X=\left[x_1,x_2,\ldots\text{}x_m\right]^\mathsf{T}) of size ![](http://latex.codecogs.com/svg.latex?\(m,k\)) is a set consisting of ![](http://latex.codecogs.com/svg.latex?m) components (each of which is of dimension ![](http://latex.codecogs.com/svg.latex?k)), is said to be permutation invariant if permutation of input components does not change the output of the function. Mathematically, ![](http://latex.codecogs.com/svg.latex?f(\left[x_1,x_2,\ldots\text{}x_m\right]^\mathsf{T})=f(M\left[x_1,x_2,\ldots\text{}x_m\right]^\mathsf{T})), where ![](http://latex.codecogs.com/svg.latex?M) is the permutation matrix of size ![](http://latex.codecogs.com/svg.latex?\(m,m\)), which is a binary matrix that has exactly a single unit value in every row and column and zeros everywhere else. + +**Permutation Equivariant Function.** Similarly, a function ![](http://latex.codecogs.com/svg.latex?f:X\rightarrow\text{Y}) is permutation equivariant if permutation of input components permutes the output components with the same permutation ![](http://latex.codecogs.com/svg.latex?M). Mathematically, ![](http://latex.codecogs.com/svg.latex?f(M\left[x_1,x_2,\ldots\text{}x_m\right]^\mathsf{T})=M\left[y_1,y_2,\ldots\text{}y_m\right]^\mathsf{T}). + +### 1.2 Why Permutation Invariant Matters? + +In MARL, the environments typically consist of ![](http://latex.codecogs.com/svg.latex?m) components, including ![](http://latex.codecogs.com/svg.latex?n) learning agents and ![](http://latex.codecogs.com/svg.latex?m-n) non-player characters. Therefore, the states, observations are factorizable as sets of ![](http://latex.codecogs.com/svg.latex?m) components ![](http://latex.codecogs.com/svg.latex?\left\[x_1,\ldots\text{}x_m\right\]), where each component ![](http://latex.codecogs.com/svg.latex?x_i) represents an atomic semantic meaning (e.g., agent ![](http://latex.codecogs.com/svg.latex?i)'s features) whose dimension is ![](http://latex.codecogs.com/svg.latex?k). Because shuffling the order of ![](http://latex.codecogs.com/svg.latex?m) components does not change the information of the set, one would expect many functions, e.g., the policy function ![](http://latex.codecogs.com/svg.latex?\pi_i(a_i|o_i)), possess permutation invariance and permutation equivariance. These properties can be exploited to design more efficient MARL algorithms, especially when the ![](http://latex.codecogs.com/svg.latex?m) components are homogeneous, i.e., semantically identical (belonging to the same type, having identical feature spaces, action spaces and reward functions). + +Taking ![](http://latex.codecogs.com/svg.latex?Q_i(a_i|o_i)) as an example, the input is the observation ![](http://latex.codecogs.com/svg.latex?o_i=\left[x_1,\ldots\text{}x_m\right]), and the outputs are Q-values of all actions in ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i). Since the ![](http://latex.codecogs.com/svg.latex?m) components are homogeneous, they have the same feature space, i.e., ![](http://latex.codecogs.com/svg.latex?\forall\text{}x_i\in\mathcal{X}). Thus, the size of an **fixedly ordered representation** of ![](http://latex.codecogs.com/svg.latex?o_i) is ![](http://latex.codecogs.com/svg.latex?|\mathcal{X}|^m). In contrast, using a **permutation invariant representation**, i.e., removing the influence of the input order, could reduce the size of the observation space by a factor of ![](http://latex.codecogs.com/svg.latex?\frac{1}{m!}). As the number of homogeneous components increases, the removal of these redundancies results in a much smaller search space, upon which we could more easily learn a policy. + +Our objective is to design more flexible **Agent Permutation Invariant** (**API**) and **Agent Permutation Equivariant** (**APE**) models to greatly reduce the sample complexity of MARL algorithms. Also taking ![](http://latex.codecogs.com/svg.latex?Q_i(a_i|o_i)) as the example, if there is a direct correspondence between the action Q-value in output and the component in input ![](http://latex.codecogs.com/svg.latex?o_i), then ![](http://latex.codecogs.com/svg.latex?Q_i(a_i|o_i)) for these actions should be permutation equivariant; otherwise, ![](http://latex.codecogs.com/svg.latex?Q_i(a_i|o_i)) should be permutation invariant. + +API_APE_function + +Note that this is very common for many multi-agent settings. For example, as illustrated in the above Figure, in the challenging [StarCraft II micromanagement benchmark (SMAC)](https://github.com/oxwhirl/smac), the input set ![](http://latex.codecogs.com/svg.latex?o_i=\left[x_1,\ldots\text{}x_m\right]) could be divided into 2 groups: an ally group ![](http://latex.codecogs.com/svg.latex?o_i^{\text{ally}}) and an enemy group ![](http://latex.codecogs.com/svg.latex?o_i^{\text{enemy}}). The output Q-values of the actions could be divided into 2 groups as well: Q-values for move actions ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{move}), i.e., ![](http://latex.codecogs.com/svg.latex?\left[\text{noop},\text{stop},\uparrow,\rightarrow,\downarrow,\leftarrow\right]), and attack actions ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{attack}). Since there is a one-to-one correspondence between the elements in ![](http://latex.codecogs.com/svg.latex?o_i^{\text{enemy}}) and ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{attack}), the Q-values of ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{attack}) should be equivariant to the permutations of ![](http://latex.codecogs.com/svg.latex?o_i^{\text{enemy}}), while the Q-values of ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{move}) should be invariant to the permutations of the whole set ![](http://latex.codecogs.com/svg.latex?o_i). Overall, a desired model of ![](http://latex.codecogs.com/svg.latex?Q_i(o_i)) should be both permutation invariant and permutation equivariance. + + + +## 2. Model Architecture of API-HyPerNetwork (API-HPN) + +![Agent permutation invariant network with hypernetworks](./figure/API-HPN.png) + +API-HPN incorporates [hypernetworks](https://arxiv.org/pdf/1609.09106) to generate different weights ![](http://latex.codecogs.com/svg.latex?W_i)s for different input components ![](http://latex.codecogs.com/svg.latex?x_i)s to improve representational capacity while ensuring the same ![](http://latex.codecogs.com/svg.latex?x_i) always be assigned with the same weight ![](http://latex.codecogs.com/svg.latex?W_i). The architecture of our API-HPN is shown in the above Figure (b). We also take the ![](http://latex.codecogs.com/svg.latex?Q_i(o_i)) as an example. The model mainly composes of two modules: + +**Agent Permutation Invariant Input Layer.** [hypernetworks](https://arxiv.org/pdf/1609.09106) are a family of neural architectures which use one network, known as hypernetwork, to generate the weights for another network. In our setting, the hypernetwork is utilized to generate a different ![](http://latex.codecogs.com/svg.latex?W_i) for each ![](http://latex.codecogs.com/svg.latex?x_i) of the input set ![](http://latex.codecogs.com/svg.latex?X_j). As shown in above Figure (b), ![](http://latex.codecogs.com/svg.latex?X_j) (which can be viewed as a batch of ![](http://latex.codecogs.com/svg.latex?m) ![](http://latex.codecogs.com/svg.latex?x_i)s each of which is of dimension ![](http://latex.codecogs.com/svg.latex?k), represented by different shades of blue) is firstly fed into a shared hypernetwork (marked in yellow), whose input size is ![](http://latex.codecogs.com/svg.latex?k) and output size is ![](http://latex.codecogs.com/svg.latex?k*h). Then, the corresponding outputs are reshaped to ![](http://latex.codecogs.com/svg.latex?\[k,h\]) and serve as the submodule weights ![](http://latex.codecogs.com/svg.latex?W_i)s of the normal FC layer (see Figure (a)). Note that different ![](http://latex.codecogs.com/svg.latex?x_i)s will generate different ![](http://latex.codecogs.com/svg.latex?W_i)s and the same ![](http://latex.codecogs.com/svg.latex?x_i) will always correspond to the same ![](http://latex.codecogs.com/svg.latex?W_i). Then, each ![](http://latex.codecogs.com/svg.latex?x_i) is multiplied by ![](http://latex.codecogs.com/svg.latex?W_i) and all multiplication results and the bias ![](http://latex.codecogs.com/svg.latex?b) are summed together to get the output. Since each element ![](http://latex.codecogs.com/svg.latex?x_i) is processed separately by its corresponding ![](http://latex.codecogs.com/svg.latex?W_i) and then merged by a permutation invariant 'sum' function, the permutation invariance is reserved. + +**Agent Permutation Equivariance Output Layer.** Similarly, to keep the whole network permutation equivariance, the submodular weights and bias of the agent-related actions in the output layer, e.g., ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_i^\text{attack}) of SMAC, are also generated by a hypernetwork. As mentioned above, the input ![](http://latex.codecogs.com/svg.latex?x_i) and output ![](http://latex.codecogs.com/svg.latex?W_i) of the hypernetwork always correspond one-to-one, so the input order change will result in the same output order change, thus achieving permutation equivariance. + +We emphasize that API-HPN is a general design and can be easily integrated into existing MARL algorithms (e.g., [VDN](https://arxiv.org/pdf/1706.05296?ref=https://githubhelp.com), [QMIX](http://proceedings.mlr.press/v80/rashid18a/rashid18a.pdf), [MADDPG](https://proceedings.neurips.cc/paper/2017/file/68a9750337a418a86fe06c1991a1d64c-Paper.pdf), [MAPPO](https://arxiv.org/pdf/2103.01955?ref=https://githubhelp.com)) to boost the learning speed as well as the converged performance. All parameters of API-HPN are simply trained end-to-end with backpropagation according to the corresponding RL loss function. + + + +## 3. Experiments + +### 3.1 Experimental Setups + +We mainly evaluate our methods in the challenging StarCraft II micromanagement benchmark [(SMAC)](https://github.com/oxwhirl/smac). + +**** + +``` +StarCraft 2 version: SC2.4.10. difficulty: 7. +``` +### 3.2 Evaluation Metric + +### 3.3 Code Implementations and Structure + +### 3.4 Results + +#### 3.4.1 Comparison with previous SOTA + +![The Full Comparison of API-HPN with SOTA on SMAC](./figure/exp_comparison_with_SOTA.png) + +#### 3.4.2 Comparison with baselines considering permutation invariance and permutation equivariant property + +![Comparison with Related Baselines](./figure/exp_comparison_with_baselines.png) + +#### 3.4.3 Ablation Studies + +![ablation](./figure/exp_ablation.png) + + + + +| Senarios | Difficulty | API-QMIX | +|----------------|:----------:|:----------------------------------:| +| 8m_vs_9m | Hard | **100%** | +| 5m_vs_6m | Hard | **100%** | +| 3s_vs_5z | Hard | **100%** | +| bane_vs_bane | Hard | **100%** | +| 2c_vs_64zg | Hard | **100%** | +| corridor | Super Hard | **100%** | +| MMM2 | Super Hard | **100%** | +| 3s5z_vs_3s6z | Super Hard |**100%** | +| 27m_vs_30m | Super Hard | **100%** | +| 6h_vs_8z | Super Hard | **98%** | + + + +## 4. How to use the code? + +### 4.1 Detailed Command line tool to reproduce all experimental results + + + +**Run an experiment** + +```shell +# For SMAC, take the 5m_vs_6m scenario for example. +CUDA_VISIBLE_DEVICES="0" python src/main.py --config=api_vdn --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 + +CUDA_VISIBLE_DEVICES="1" python src/main.py --config=api_qmix --env-config=sc2 with env_args.map_name=5m_vs_6m obs_agent_id=True obs_last_action=False runner=parallel batch_size_run=8 buffer_size=5000 t_max=10050000 epsilon_anneal_time=100000 batch_size=128 td_lambda=0.6 +``` + + + + +The config files act as defaults for an algorithm or environment. + +They are all located in `src/config`. +`--config` refers to the config files in `src/config/algs` +`--env-config` refers to the config files in `src/config/envs` + + + +# Citation +``` +@article{, + title={API: Boosting Multi-Agent Reinforcement Learning via Agent-Permutation-Invariant Networks}, + author={}, + year={2022}, + eprint={}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + diff --git a/doc/figure/API-HPN.png b/doc/figure/API-HPN.png new file mode 100644 index 0000000000000000000000000000000000000000..ce1f7dda5350833b070c2ebf49057e43711f98fe GIT binary patch literal 121884 zcmY&=2Rzm7`~MNL!m+a*GBe6N_9lc-=0V8_8OP2pd&}O+3>nELieqQ5%)_xs9Fmdo z{~Vs@`F;QO>h$`2s&n7>HQv{Cy|3#o;*pjrF(Dlx2m~Tld!VEX0^uS+pc`!XH-R^N z0=FfA->_VDRTV%LgAAL%1CEWnraTDrDURsu87}Zl;QYYI6$B#b!2H7McKTom0$moW zDak+fGW+?3D2;L={c`Anxq8|UW%pg?)}Uk3OW_Qbkrx)iLvzdk z=`Y~t*6#YNR?>$C?oZsGc$&(%Z%Gf}0~`ClA6!^02(15m1c8!pz^MOy2C3h`hyQ;s z*s+44|N9~^0i?k2zc1v}KoFAueev#2AQ%txVlP74=g;t9wxn%;3SW)myo5t6gSbU> z|K(O_|8%Qg^L_q~%c&SP)kW)>mXB{fXqkw?VgixH^!%K>V=48`nYK~8l=;9@^q9xz z`BmHI6Ri-dnSBpWAqV-iqAvlL{t~{&&Ree7n6;D`|JgT<@5RY_D5b^H`BquwD3L&m 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Install Python dependencies +echo 'Install PyTorch and Python dependencies...' +# conda create -n pymarl python=3.8 -y +# conda activate pymarl + +conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia -y +pip install sacred numpy scipy gym==0.10.8 matplotlib seaborn \ + pyyaml==5.3.1 pygame pytest probscale imageio snakeviz tensorboard-logger + +# pip install git+https://github.com/oxwhirl/smac.git +# Do not need install SMAC anymore. We have integrated SMAC-V1 and SMAC-V2 in pymarl3/envs. +pip install "protobuf<3.21" +pip install "pysc2>=3.0.0" +pip install "s2clientprotocol>=4.10.1.75800.0" +pip install "absl-py>=0.1.0" diff --git a/install_sc2.sh b/install_sc2.sh new file mode 100644 index 0000000..0e0eb99 --- /dev/null +++ b/install_sc2.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# Install SC2 and add the custom maps + +# Clone the source code. +#git clone git@github.com:tjuHaoXiaotian/pymarl3.git +export PYMARL3_CODE_DIR=$(pwd) + +# 1. Install StarCraftII +echo 'Install StarCraftII...' +cd "$HOME" +export SC2PATH="$HOME/StarCraftII" +echo 'SC2PATH is set to '$SC2PATH +if [ ! -d $SC2PATH ]; then + echo 'StarCraftII is not installed. Installing now ...'; + wget http://blzdistsc2-a.akamaihd.net/Linux/SC2.4.10.zip + unzip -P iagreetotheeula SC2.4.10.zip +else + echo 'StarCraftII is already installed.' +fi + +# 2. Install the custom maps + +# Copy the maps to the target dir. +echo 'Install SMACV1 and SMACV2 maps...' +MAP_DIR="$SC2PATH/Maps/" +if [ ! -d "$MAP_DIR/SMAC_Maps" ]; then + echo 'MAP_DIR is set to '$MAP_DIR + if [ ! -d $MAP_DIR ]; then + mkdir -p $MAP_DIR + fi + cp -r "$PYMARL3_CODE_DIR/src/envs/smac_v2/official/maps/SMAC_Maps" $MAP_DIR +else + echo 'SMACV1 and SMACV2 maps are already installed.' +fi +echo 'StarCraft II and SMAC maps are installed.' \ No newline at end of file diff --git a/obs_config.json b/obs_config.json new file mode 100644 index 0000000..193a782 --- /dev/null +++ b/obs_config.json @@ -0,0 +1,329 @@ +{ + "2c_vs_64zg_obs" : { + "model_input_size": 404, + "env_obs_size": 332, + "n_agent": 2, + "n_enemy": 64, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [64, 5]}, + "2": {"name": "ally_feats", "size": [1, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 70}, + "5": {"name": "agent_id", "size": 2} + } + }, + + "2m_vs_1z_obs" : { + "model_input_size": 25, + "env_obs_size": 16, + "n_agent": 2, + "n_enemy": 1, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [1, 6]}, + "2": {"name": "ally_feats", "size": [1, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 7}, + "5": {"name": "agent_id", "size": 2} + } + }, + + "2s_vs_1sc_obs" : { + "model_input_size": 26, + "env_obs_size": 17, + "n_agent": 2, + "n_enemy": 1, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [1, 5]}, + "2": {"name": "ally_feats", "size": [1, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 7}, + "5": {"name": "agent_id", "size": 2} + } + }, + + "2s3z_obs" : { + "model_input_size": 96, + "env_obs_size": 80, + "n_agent": 5, + "n_enemy": 5, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [5, 8]}, + "2": {"name": "ally_feats", "size": [4, 8]}, + "3": {"name": "own_feats", "size": 4}, + "4": {"name": "last_action", "size": 11}, + "5": {"name": "agent_id", "size": 5} + } + }, + + "3m_obs" : { + "model_input_size": 42, + "env_obs_size": 30, + "n_agent": 3, + "n_enemy": 3, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [3, 5]}, + "2": {"name": "ally_feats", "size": [2, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 9}, + "5": {"name": "agent_id", "size": 3} + } + }, + + "3s_vs_3z_obs" : { + "model_input_size": 48, + "env_obs_size": 36, + "n_agent": 3, + "n_enemy": 3, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [3, 6]}, + "2": {"name": "ally_feats", "size": [2, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 9}, + "5": {"name": "agent_id", "size": 3} + } + }, + + "3s_vs_4z_obs" : { + "model_input_size": 55, + "env_obs_size": 42, + "n_agent": 3, + "n_enemy": 4, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [4, 6]}, + "2": {"name": "ally_feats", "size": [2, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 10}, + "5": {"name": "agent_id", "size": 3} + } + }, + + "3s_vs_5z_obs" : { + "model_input_size": 62, + "env_obs_size": 48, + "n_agent": 3, + "n_enemy": 5, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [5, 6]}, + "2": {"name": "ally_feats", "size": [2, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 11}, + "5": {"name": "agent_id", "size": 3} + } + }, + + "3s5z_obs" : { + "model_input_size": 150, + "env_obs_size": 128, + "n_agent": 8, + "n_enemy": 8, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [8, 8]}, + "2": {"name": "ally_feats", "size": [7, 8]}, + "3": {"name": "own_feats", "size": 4}, + "4": {"name": "last_action", "size": 14}, + "5": {"name": "agent_id", "size": 8} + } + }, + + "3s5z_vs_3s6z_obs" : { + "model_input_size": 159, + "env_obs_size": 136, + "n_agent": 8, + "n_enemy": 9, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [9, 8]}, + "2": {"name": "ally_feats", "size": [7, 8]}, + "3": {"name": "own_feats", "size": 4}, + "4": {"name": "last_action", "size": 15}, + "5": {"name": "agent_id", "size": 8} + } + }, + + "5m_vs_6m_obs" : { + "model_input_size": 72, + "env_obs_size": 55, + "n_agent": 5, "n_enemy": 6, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [6, 5]}, + "2": {"name": "ally_feats", "size": [4, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 12}, + "5": {"name": "agent_id", "size": 5}} + }, + + "6h_vs_8z_obs" : { + "model_input_size": 98, + "env_obs_size": 78, + "n_agent": 6, + "n_enemy": 8, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [8, 6]}, + "2": {"name": "ally_feats", "size": [5, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 14}, + "5": {"name": "agent_id", "size": 6} + } + }, + + "8m_obs" : { + "model_input_size": 102, + "env_obs_size": 80, + "n_agent": 8, + "n_enemy": 8, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [8, 5]}, + "2": {"name": "ally_feats", "size": [7, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 14}, + "5": {"name": "agent_id", "size": 8} + } + }, + + "8m_vs_9m_obs" : { + "model_input_size": 108, + "env_obs_size": 85, + "n_agent": 8, + "n_enemy": 9, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [9, 5]}, + "2": {"name": "ally_feats", "size": [7, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 15}, + "5": {"name": "agent_id", "size": 8} + } + }, + + "10m_vs_11m_obs" : { + "model_input_size": 132, + "env_obs_size": 105, + "n_agent": 10, + "n_enemy": 11, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [11, 5]}, + "2": {"name": "ally_feats", "size": [9, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 17}, + "5": {"name": "agent_id", "size": 10} + } + }, + + "25m_obs" : { + "model_input_size": 306, + "env_obs_size": 250, + "n_agent": 25, + "n_enemy": 25, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [25, 5]}, + "2": {"name": "ally_feats", "size": [24, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 31}, + "5": {"name": "agent_id", "size": 25} + } + }, + + "27m_vs_30m_obs" : { + "model_input_size": 348, + "env_obs_size": 285, + "n_agent": 27, + "n_enemy": 30, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [30, 5]}, + "2": {"name": "ally_feats", "size": [26, 5]}, + "3": {"name": "own_feats", "size": 1}, + "4": {"name": "last_action", "size": 36}, + "5": {"name": "agent_id", "size": 27} + } + }, + + "bane_vs_bane_obs" : { + "model_input_size": 390, + "env_obs_size": 336, + "n_agent": 24, + "n_enemy": 24, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [24, 7]}, + "2": {"name": "ally_feats", "size": [23, 7]}, + "3": {"name": "own_feats", "size": 3}, + "4": {"name": "last_action", "size": 30}, + "5": {"name": "agent_id", "size": 24} + } + }, + + "corridor_obs" : { + "model_input_size": 192, + "env_obs_size": 156, + "n_agent": 6, + "n_enemy": 24, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [24, 5]}, + "2": {"name": "ally_feats", "size": [5, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 30}, + "5": {"name": "agent_id", "size": 6} + } + }, + + "MMM_obs" : { + "model_input_size": 186, + "env_obs_size": 160, + "n_agent": 10, + "n_enemy": 10, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [10, 8]}, + "2": {"name": "ally_feats", "size": [9, 8]}, + "3": {"name": "own_feats", "size": 4}, + "4": {"name": "last_action", "size": 16}, + "5": {"name": "agent_id", "size": 10} + } + }, + + "MMM2_obs" : { + "model_input_size": 204, + "env_obs_size": 176, + "n_agent": 10, + "n_enemy": 12, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [12, 8]}, + "2": {"name": "ally_feats", "size": [9, 8]}, + "3": {"name": "own_feats", "size": 4}, + "4": {"name": "last_action", "size": 18}, + "5": {"name": "agent_id", "size": 10} + } + }, + + "so_many_baneling_obs" : { + "model_input_size": 247, + "env_obs_size": 202, + "n_agent": 7, + "n_enemy": 32, + "model_input_compose": { + "0": {"name": "move_feats", "size": 4}, + "1": {"name": "enemy_feats", "size": [32, 5]}, + "2": {"name": "ally_feats", "size": [6, 6]}, + "3": {"name": "own_feats", "size": 2}, + "4": {"name": "last_action", "size": 38}, + "5": {"name": "agent_id", "size": 7} + } + } +} \ No newline at end of file diff --git a/plotsmac.py b/plotsmac.py new file mode 100644 index 0000000..c8a4234 --- /dev/null +++ b/plotsmac.py @@ -0,0 +1,117 @@ +import json +import matplotlib.pyplot as plt +import numpy as np +import tkinter as tk +from tkinter import ttk +from scipy.signal import savgol_filter + +# 讀取多個 JSON 檔案 +def load_data(file_paths): + all_data = [] + for file_path in file_paths: + with open(file_path) as f: + data = json.load(f) + # 處理特殊的數據格式（numpy.float64 對象） + processed_data = {} + for key, value in data.items(): + if isinstance(value, list): + # 檢查是否包含字典格式的數值 + if value and isinstance(value[0], dict) and 'value' in value[0]: + processed_data[key] = [item['value'] for item in value] + else: + processed_data[key] = value + else: + processed_data[key] = value + all_data.append(processed_data) + return all_data + +def smooth(y, window_length=51, polyorder=3): + return savgol_filter(y, window_length, polyorder) + +# 定義要繪製的數據 +def plot_data(data_list, keys, name_list, battle_name, smooth_window=2): + for key in keys: + # 創建一個圖形 + fig, ax = plt.subplots() + # 設置坐標軸背景顏色 + ax.set_facecolor('lightyellow') + # 設置網格顏色 + ax.grid(color='green', linestyle='--', linewidth=0.5) + + for data, name in zip(data_list, name_list): + if key in data: + x = data[key + '_T'] + y = data[key] + y_smooth = smooth(y) + ax.plot(x, y_smooth, label=name) + #ax.plot(data[key + '_T'], data[key], label=name) + ax.set_xlabel('Time Steps') + ax.set_ylabel(key) + ax.set_title(battle_name) + ax.legend() + + plt.show() + +def create_dynamic_window(data_list, keys, name_list, battle_name): + root = tk.Tk() + root.title("動態選擇要顯示的數據") + + # 創建左側的選擇面板 + select_frame = ttk.Frame(root) + select_frame.pack(side=tk.LEFT, fill=tk.Y, padx=10, pady=5) + + # 創建變量來存儲選擇狀態 + vars = [] + + def update_plot(*args): + # 獲取選中的數據 + selected_names = [name for name, var in zip(name_list, vars) if var.get()] + filtered_data = [data for data, name in zip(data_list, name_list) if name in selected_names] + filtered_names = [name for name in name_list if name in selected_names] + + # 清除所有現有的圖表 + plt.close('all') + # 重新繪製圖表 + if filtered_data: # 確保至少選擇了一個數據 + plot_data(filtered_data, keys, filtered_names, battle_name) + + # 創建複選框 + for name in name_list: + var = tk.BooleanVar(value=True) # 默認全選 + var.trace('w', update_plot) # 添加跟踪器，當值改變時更新圖表 + vars.append(var) + cb = ttk.Checkbutton(select_frame, text=name, variable=var) + cb.pack(anchor='w', padx=5, pady=2) + + # 添加全選/取消全選按鈕 + def select_all(): + for var in vars: + var.set(True) + + def deselect_all(): + for var in vars: + var.set(False) + + ttk.Button(select_frame, text="全選", command=select_all).pack(pady=5) + ttk.Button(select_frame, text="取消全選", command=deselect_all).pack(pady=5) + + # 初始繪圖 + update_plot() + + root.mainloop() + +# 使用者選擇要繪製的數據 +file_paths = ['results/sacred/10gen_protoss/feudal/5/info.json', + 'results/sacred/10gen_protoss/qmix/6/info.json', + ] + +data_list = load_data(file_paths) +selected_keys = ['test_battle_won_mean','return_mean',"worker_loss","loss_td","manager_loss"] +#selected_keys = ['battle_won_mean', 'loss'] +name_list = ['feudal', 'qmix'] + + +battle_name = '5protoss' + +# 使用新的動態選擇窗口 +create_dynamic_window(data_list, selected_keys, name_list, battle_name) diff --git a/results/models/sc2_v2_10gen_protoss-obs_aid=1-obs_act=0-conic_fov=0/algo=feudal-agent=feudal/env_n=4/rnn_dim=64-2bs=5000_128-tdlambda=0.6-epdec_0.05=100k/feudal__2025-01-06_04-17-39/2000120/manager_agent.torch 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b/results/sacred/10gen_protoss/feudal/5/run.json @@ -0,0 +1,124 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 318, in run_sequential\n sys.stdout.flush()\n", + "NameError: name 'sys' is not defined\n" + ], + "heartbeat": "2025-01-06T04:05:20.393530", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": false, + "runner": "parallel", + "t_max": 4050000, + "td_lambda": 0.6, + "use_tensorboard": false + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6", + "use_tensorboard=False" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T20:17:39.546682", + "status": "FAILED", + "stop_time": "2025-01-06T04:05:21.070681" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py b/results/sacred/10gen_protoss/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py new file mode 100644 index 0000000..5393b7f --- /dev/null +++ b/results/sacred/10gen_protoss/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py @@ -0,0 +1,68 @@ +from collections import defaultdict +import logging +import numpy as np +import torch as th + +class Logger: + def __init__(self, console_logger): + self.console_logger = console_logger + + self.use_tb = False + self.use_sacred = False + self.use_hdf = False + + self.stats = defaultdict(lambda: []) + + def setup_tb(self, directory_name): + # Import here so it doesn't have to be installed if you don't use it + from tensorboard_logger import configure, log_value + configure(directory_name) + self.tb_logger = log_value + self.use_tb = True + + def setup_sacred(self, sacred_run_dict): + self.sacred_info = sacred_run_dict.info + self.use_sacred = True + + def log_stat(self, key, value, t, to_sacred=True): + self.stats[key].append((t, value)) + + if self.use_tb: + self.tb_logger(key, value, t) + + if self.use_sacred and to_sacred: + if key in self.sacred_info: + self.sacred_info["{}_T".format(key)].append(t) + self.sacred_info[key].append(value) + else: + self.sacred_info["{}_T".format(key)] = [t] + self.sacred_info[key] = [value] + + def print_recent_stats(self): + log_str = "Recent Stats | t_env: {:>10} | Episode: {:>8}\n".format(*self.stats["episode"][-1]) + i = 0 + for (k, v) in sorted(self.stats.items()): + if k == "episode": + continue + i += 1 + window = 5 if k != "epsilon" else 1 + item = "{:.4f}".format(th.mean(th.tensor([float(x[1]) for x in self.stats[k][-window:]]))) + log_str += "{:<25}{:>8}".format(k + ":", item) + log_str += "\n" if i % 4 == 0 else "\t" + self.console_logger.info(log_str) + # Reset stats to avoid accumulating logs in memory + self.stats = defaultdict(lambda: []) + + +# set up a custom logger +def get_logger(): + logger = logging.getLogger() + logger.handlers = [] + ch = logging.StreamHandler() + formatter = logging.Formatter('[%(levelname)s %(asctime)s] %(name)s %(message)s', '%H:%M:%S') + ch.setFormatter(formatter) + logger.addHandler(ch) + logger.setLevel('DEBUG') + + return logger + diff --git a/results/sacred/10gen_protoss/feudal/_sources/main_654daaa6534bcee62784d639ea63e51d.py b/results/sacred/10gen_protoss/feudal/_sources/main_654daaa6534bcee62784d639ea63e51d.py new file mode 100644 index 0000000..199f3a0 --- /dev/null +++ b/results/sacred/10gen_protoss/feudal/_sources/main_654daaa6534bcee62784d639ea63e51d.py @@ -0,0 +1,124 @@ +import random + +import numpy as np +import os +import collections +from os.path import dirname, abspath, join +from copy import deepcopy +from sacred import Experiment, SETTINGS +from sacred.observers import FileStorageObserver +from sacred.utils import apply_backspaces_and_linefeeds +import sys +import torch as th +from utils.logging import get_logger +import yaml +import collections.abc + +from run import REGISTRY as run_REGISTRY + +SETTINGS['CAPTURE_MODE'] = "no" # set to "no" if you want to see stdout/stderr in console "fd" or "no" +logger = get_logger() + +ex = Experiment("pymarl") +ex.logger = logger +ex.captured_out_filter = apply_backspaces_and_linefeeds + +results_path = join(dirname(dirname(abspath(__file__)))) + + +@ex.main +def my_main(_run, _config, _log): + # Setting the random seed throughout the modules + config = config_copy(_config) + random.seed(config["seed"]) + np.random.seed(config["seed"]) + th.manual_seed(config["seed"]) + th.cuda.manual_seed(config["seed"]) + # th.cuda.manual_seed_all(config["seed"]) + th.backends.cudnn.deterministic = True # cudnn + + + config['env_args']['seed'] = config["seed"] + + # run + run_REGISTRY[_config['run']](_run, config, _log) + + +def _get_config(params, arg_name, subfolder): + config_name = None + for _i, _v in enumerate(params): + if _v.split("=")[0] == arg_name: + config_name = _v.split("=")[1] + del params[_i] + break + + if config_name is not None: + with open(os.path.join(os.path.dirname(__file__), "config", subfolder, "{}.yaml".format(config_name)), + "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "{}.yaml error: {}".format(config_name, exc) + return config_dict + + +def recursive_dict_update(d, u): + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = recursive_dict_update(d.get(k, {}), v) + else: + d[k] = v + return d + + +def config_copy(config): + if isinstance(config, dict): + return {k: config_copy(v) for k, v in config.items()} + elif isinstance(config, list): + return [config_copy(v) for v in config] + else: + return deepcopy(config) + + +def parse_command(params, key, default): + result = default + for _i, _v in enumerate(params): + if _v.split("=")[0].strip() == key: + result = _v[_v.index('=') + 1:].strip() + break + return result + + +if __name__ == '__main__': + params = deepcopy(sys.argv) + + # Get the defaults from default.yaml + with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "default.yaml error: {}".format(exc) + + # Load algorithm and env base configs + env_config = _get_config(params, "--env-config", "envs") + alg_config = _get_config(params, "--config", "algs") + # config_dict = {**config_dict, **env_config, **alg_config} + config_dict = recursive_dict_update(config_dict, env_config) + config_dict = recursive_dict_update(config_dict, alg_config) + + # now add all the config to sacred + ex.add_config(config_dict) + + # Save to disk by default for sacred + map_name = parse_command(params, "env_args.map_name", config_dict['env_args']['map_name']) + algo_name = parse_command(params, "name", config_dict['name']) + local_results_path = parse_command(params, "local_results_path", config_dict['local_results_path']) + file_obs_path = join(results_path, local_results_path, "sacred", map_name, algo_name) + + logger.info("Saving to FileStorageObserver in {}.".format(file_obs_path)) + ex.observers.append(FileStorageObserver.create(file_obs_path)) + + ex.run_commandline(params) + + # flush + sys.stdout.flush() diff --git a/results/sacred/10gen_protoss/qmix/1/config.json b/results/sacred/10gen_protoss/qmix/1/config.json new file mode 100644 index 0000000..5ee7c0e --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/1/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + }, + "team_gen": { + "dist_type": "weighted_teams", + "exception_unit_types": [ + "colossus" + ], + "observe": true, + "unit_types": [ + "stalker", + "zealot", + "colossus" + ], + "weights": [ + 0.45, + 0.45, + 0.1 + ] + } + }, + "change_fov_with_move": false, + "conic_fov": false, + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "fully_observable": false, + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "kill_unit_step_mul": 2, + "map_name": "10gen_protoss", + "move_amount": 2, + "num_fov_actions": 12, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_own_pos": true, + "obs_pathing_grid": false, + "obs_starcraft": true, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": false, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 163470727, + "t_max": 4050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/1/cout.txt b/results/sacred/10gen_protoss/qmix/1/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/10gen_protoss/qmix/1/metrics.json b/results/sacred/10gen_protoss/qmix/1/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/1/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/1/run.json b/results/sacred/10gen_protoss/qmix/1/run.json new file mode 100644 index 0000000..9db1eaa --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/1/run.json @@ -0,0 +1,124 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 122, in run\n logger.setup_tb(tb_exp_direc)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\utils\\logging.py\", line 19, in setup_tb\n configure(directory_name)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 296, in configure\n _default_logger = Logger(logdir, flush_secs=flush_secs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 51, in __init__\n self._writer = open(filename, 'wb')\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Taiyo\\\\Desktop\\\\SMAC V2\\\\pymarl3\\\\results\\\\tb_logs\\\\sc2_v2_10gen_protoss-obs_aid=1-obs_act=0-conic_fov=0\\\\algo=qmix-agent=n_rnn\\\\env_n=4\\\\mixer=qmix\\\\rnn_dim=64-2bs=5000_128-tdlambda=0.6-epdec_0.05=100k\\\\qmix__2025-01-06_00-15-29\\\\events.out.tfevents.1736093730.Taiyopen'\n" + ], + "heartbeat": "2025-01-05T16:15:30.002599", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": false, + "runner": "parallel", + "t_max": 4050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:15:29.896110", + "status": "FAILED", + "stop_time": "2025-01-05T16:15:30.004651" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/2/config.json b/results/sacred/10gen_protoss/qmix/2/config.json new file mode 100644 index 0000000..e979462 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/2/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + }, + "team_gen": { + "dist_type": "weighted_teams", + "exception_unit_types": [ + "colossus" + ], + "observe": true, + "unit_types": [ + "stalker", + "zealot", + "colossus" + ], + "weights": [ + 0.45, + 0.45, + 0.1 + ] + } + }, + "change_fov_with_move": false, + "conic_fov": false, + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "fully_observable": false, + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "kill_unit_step_mul": 2, + "map_name": "10gen_protoss", + "move_amount": 2, + "num_fov_actions": 12, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_own_pos": true, + "obs_pathing_grid": false, + "obs_starcraft": true, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": false, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 790531776, + "t_max": 4050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/2/cout.txt b/results/sacred/10gen_protoss/qmix/2/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/10gen_protoss/qmix/2/metrics.json b/results/sacred/10gen_protoss/qmix/2/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/2/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/2/run.json b/results/sacred/10gen_protoss/qmix/2/run.json new file mode 100644 index 0000000..a21a951 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/2/run.json @@ -0,0 +1,124 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 122, in run\n logger.setup_tb(tb_exp_direc)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\utils\\logging.py\", line 19, in setup_tb\n configure(directory_name)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 296, in configure\n _default_logger = Logger(logdir, flush_secs=flush_secs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 51, in __init__\n self._writer = open(filename, 'wb')\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Taiyo\\\\Desktop\\\\SMAC V2\\\\pymarl3\\\\results\\\\tb_logs\\\\sc2_v2_10gen_protoss-obs_aid=1-obs_act=0-conic_fov=0\\\\algo=qmix-agent=n_rnn\\\\env_n=4\\\\mixer=qmix\\\\rnn_dim=64-2bs=5000_128-tdlambda=0.6-epdec_0.05=100k\\\\qmix__2025-01-06_00-15-40\\\\events.out.tfevents.1736093740.Taiyopen'\n" + ], + "heartbeat": "2025-01-05T16:15:40.344931", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": false, + "runner": "parallel", + "t_max": 4050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:15:40.272388", + "status": "FAILED", + "stop_time": "2025-01-05T16:15:40.346943" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/3/config.json b/results/sacred/10gen_protoss/qmix/3/config.json new file mode 100644 index 0000000..980c7f2 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/3/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + }, + "team_gen": { + "dist_type": "weighted_teams", + "exception_unit_types": [ + "colossus" + ], + "observe": true, + "unit_types": [ + "stalker", + "zealot", + "colossus" + ], + "weights": [ + 0.45, + 0.45, + 0.1 + ] + } + }, + "change_fov_with_move": false, + "conic_fov": false, + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "fully_observable": false, + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "kill_unit_step_mul": 2, + "map_name": "10gen_protoss", + "move_amount": 2, + "num_fov_actions": 12, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_own_pos": true, + "obs_pathing_grid": false, + "obs_starcraft": true, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": false, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 788463946, + "t_max": 4050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/3/cout.txt b/results/sacred/10gen_protoss/qmix/3/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/10gen_protoss/qmix/3/metrics.json b/results/sacred/10gen_protoss/qmix/3/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/3/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/3/run.json b/results/sacred/10gen_protoss/qmix/3/run.json new file mode 100644 index 0000000..6408a42 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/3/run.json @@ -0,0 +1,124 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 122, in run\n logger.setup_tb(tb_exp_direc)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\utils\\logging.py\", line 19, in setup_tb\n configure(directory_name)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 296, in configure\n _default_logger = Logger(logdir, flush_secs=flush_secs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 51, in __init__\n self._writer = open(filename, 'wb')\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Taiyo\\\\Desktop\\\\SMAC 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+ "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:16:48.931426", + "status": "FAILED", + "stop_time": "2025-01-05T16:16:49.018225" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/4/config.json b/results/sacred/10gen_protoss/qmix/4/config.json new file mode 100644 index 0000000..7566520 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/4/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + }, + "team_gen": { + "dist_type": "weighted_teams", + "exception_unit_types": [ + "colossus" + ], + "observe": true, + "unit_types": [ + "stalker", + "zealot", + "colossus" + ], + "weights": [ + 0.45, + 0.45, + 0.1 + ] + } + }, + "change_fov_with_move": false, + "conic_fov": false, + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "fully_observable": false, + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "kill_unit_step_mul": 2, + "map_name": "10gen_protoss", + "move_amount": 2, + "num_fov_actions": 12, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_own_pos": true, + "obs_pathing_grid": false, + "obs_starcraft": true, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": false, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 727581499, + "t_max": 4050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/4/cout.txt b/results/sacred/10gen_protoss/qmix/4/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/10gen_protoss/qmix/4/metrics.json b/results/sacred/10gen_protoss/qmix/4/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/4/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/4/run.json b/results/sacred/10gen_protoss/qmix/4/run.json new file mode 100644 index 0000000..3e1c753 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/4/run.json @@ -0,0 +1,124 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 122, in run\n logger.setup_tb(tb_exp_direc)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\utils\\logging.py\", line 19, in setup_tb\n configure(directory_name)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 296, in configure\n _default_logger = Logger(logdir, flush_secs=flush_secs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 51, in __init__\n self._writer = open(filename, 'wb')\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Taiyo\\\\Desktop\\\\SMAC V2\\\\pymarl3\\\\results\\\\tb_logs\\\\sc2_v2_10gen_protoss-obs_aid=1-obs_act=0-conic_fov=0\\\\algo=qmix-agent=n_rnn\\\\env_n=4\\\\mixer=qmix\\\\rnn_dim=64-2bs=5000_128-tdlambda=0.6-epdec_0.05=100k\\\\qmix__2025-01-06_00-23-20\\\\events.out.tfevents.1736094200.Taiyopen'\n" + ], + "heartbeat": "2025-01-05T16:23:20.246737", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": false, + "runner": "parallel", + "t_max": 4050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:23:20.174349", + "status": "FAILED", + "stop_time": "2025-01-05T16:23:20.249243" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/5/config.json b/results/sacred/10gen_protoss/qmix/5/config.json new file mode 100644 index 0000000..e88e7fc --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/5/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + }, + "team_gen": { + "dist_type": "weighted_teams", + "exception_unit_types": [ + "colossus" + ], + "observe": true, + "unit_types": [ + "stalker", + "zealot", + "colossus" + ], + "weights": [ + 0.45, + 0.45, + 0.1 + ] + } + }, + "change_fov_with_move": false, + "conic_fov": false, + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "fully_observable": false, + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "kill_unit_step_mul": 2, + "map_name": "10gen_protoss", + "move_amount": 2, + "num_fov_actions": 12, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_own_pos": true, + "obs_pathing_grid": false, + "obs_starcraft": true, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": false, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 148457271, + "t_max": 4050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/5/cout.txt b/results/sacred/10gen_protoss/qmix/5/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/10gen_protoss/qmix/5/metrics.json b/results/sacred/10gen_protoss/qmix/5/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/5/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/5/run.json b/results/sacred/10gen_protoss/qmix/5/run.json new file mode 100644 index 0000000..b5abe76 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/5/run.json @@ -0,0 +1,126 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 122, in run\n logger.setup_tb(tb_exp_direc)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\utils\\logging.py\", line 19, in setup_tb\n configure(directory_name)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 296, in configure\n _default_logger = Logger(logdir, flush_secs=flush_secs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\tensorboard_logger\\tensorboard_logger.py\", line 51, in __init__\n self._writer = open(filename, 'wb')\n", + "FileNotFoundError: [Errno 2] No such file or directory: 'C:\\\\Users\\\\Taiyo\\\\Desktop\\\\SMAC V2\\\\pymarl3\\\\results\\\\tb_logs\\\\sc2_v2_10gen_protoss-obs_aid=1-obs_act=0-conic_fov=0\\\\algo=qmix-agent=n_rnn\\\\env_n=4\\\\mixer=qmix\\\\rnn_dim=64-2bs=5000_128-tdlambda=0.6-epdec_0.05=100k\\\\qmix__2025-01-06_00-23-53\\\\events.out.tfevents.1736094233.Taiyopen'\n" + ], + "heartbeat": "2025-01-05T16:23:53.621785", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + 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"help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:23:53.552221", + "status": "FAILED", + "stop_time": "2025-01-05T16:23:53.623909" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/6/config.json b/results/sacred/10gen_protoss/qmix/6/config.json new file mode 100644 index 0000000..47acf21 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/6/config.json @@ -0,0 +1,130 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2_v2", + "env_args": { + "capability_config": { + "n_units": 5, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "map_x": 32, + "map_y": 32, + "n_enemies": 5, + "p": 0.5 + 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"44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_654daaa6534bcee62784d639ea63e51d.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "heartbeat": "2025-01-05T19:43:03.753377", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": false, + "runner": "parallel", + "t_max": 4050000, + "td_lambda": 0.6, + "use_tensorboard": false + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "obs_agent_id=True", + "obs_last_action=False", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=4050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6", + "use_tensorboard=False" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2025-01-05T16:24:03.185725", + "status": "INTERRUPTED", + "stop_time": "2025-01-05T19:43:04.003168" +} \ No newline at end of file diff --git a/results/sacred/10gen_protoss/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py b/results/sacred/10gen_protoss/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py new file mode 100644 index 0000000..5393b7f --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py @@ -0,0 +1,68 @@ +from collections import defaultdict +import logging +import numpy as np +import torch as th + +class Logger: + def __init__(self, console_logger): + self.console_logger = console_logger + + self.use_tb = False + self.use_sacred = False + self.use_hdf = False + + self.stats = defaultdict(lambda: []) + + def setup_tb(self, directory_name): + # Import here so it doesn't have to be installed if you don't use it + from tensorboard_logger import configure, log_value + configure(directory_name) + self.tb_logger = log_value + self.use_tb = True + + def setup_sacred(self, sacred_run_dict): + self.sacred_info = sacred_run_dict.info + self.use_sacred = True + + def log_stat(self, key, value, t, to_sacred=True): + self.stats[key].append((t, value)) + + if self.use_tb: + self.tb_logger(key, value, t) + + if self.use_sacred and to_sacred: + if key in self.sacred_info: + self.sacred_info["{}_T".format(key)].append(t) + self.sacred_info[key].append(value) + else: + self.sacred_info["{}_T".format(key)] = [t] + self.sacred_info[key] = [value] + + def print_recent_stats(self): + log_str = "Recent Stats | t_env: {:>10} | Episode: {:>8}\n".format(*self.stats["episode"][-1]) + i = 0 + for (k, v) in sorted(self.stats.items()): + if k == "episode": + continue + i += 1 + window = 5 if k != "epsilon" else 1 + item = "{:.4f}".format(th.mean(th.tensor([float(x[1]) for x in self.stats[k][-window:]]))) + log_str += "{:<25}{:>8}".format(k + ":", item) + log_str += "\n" if i % 4 == 0 else "\t" + self.console_logger.info(log_str) + # Reset stats to avoid accumulating logs in memory + self.stats = defaultdict(lambda: []) + + +# set up a custom logger +def get_logger(): + logger = logging.getLogger() + logger.handlers = [] + ch = logging.StreamHandler() + formatter = logging.Formatter('[%(levelname)s %(asctime)s] %(name)s %(message)s', '%H:%M:%S') + ch.setFormatter(formatter) + logger.addHandler(ch) + logger.setLevel('DEBUG') + + return logger + diff --git a/results/sacred/10gen_protoss/qmix/_sources/main_654daaa6534bcee62784d639ea63e51d.py b/results/sacred/10gen_protoss/qmix/_sources/main_654daaa6534bcee62784d639ea63e51d.py new file mode 100644 index 0000000..199f3a0 --- /dev/null +++ b/results/sacred/10gen_protoss/qmix/_sources/main_654daaa6534bcee62784d639ea63e51d.py @@ -0,0 +1,124 @@ +import random + +import numpy as np +import os +import collections +from os.path import dirname, abspath, join +from copy import deepcopy +from sacred import Experiment, SETTINGS +from sacred.observers import FileStorageObserver +from sacred.utils import apply_backspaces_and_linefeeds +import sys +import torch as th +from utils.logging import get_logger +import yaml +import collections.abc + +from run import REGISTRY as run_REGISTRY + +SETTINGS['CAPTURE_MODE'] = "no" # set to "no" if you want to see stdout/stderr in console "fd" or "no" +logger = get_logger() + +ex = Experiment("pymarl") +ex.logger = logger +ex.captured_out_filter = apply_backspaces_and_linefeeds + +results_path = join(dirname(dirname(abspath(__file__)))) + + +@ex.main +def my_main(_run, _config, _log): + # Setting the random seed throughout the modules + config = config_copy(_config) + random.seed(config["seed"]) + np.random.seed(config["seed"]) + th.manual_seed(config["seed"]) + th.cuda.manual_seed(config["seed"]) + # th.cuda.manual_seed_all(config["seed"]) + th.backends.cudnn.deterministic = True # cudnn + + + config['env_args']['seed'] = config["seed"] + + # run + run_REGISTRY[_config['run']](_run, config, _log) + + +def _get_config(params, arg_name, subfolder): + config_name = None + for _i, _v in enumerate(params): + if _v.split("=")[0] == arg_name: + config_name = _v.split("=")[1] + del params[_i] + break + + if config_name is not None: + with open(os.path.join(os.path.dirname(__file__), "config", subfolder, "{}.yaml".format(config_name)), + "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "{}.yaml error: {}".format(config_name, exc) + return config_dict + + +def recursive_dict_update(d, u): + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = recursive_dict_update(d.get(k, {}), v) + else: + d[k] = v + return d + + +def config_copy(config): + if isinstance(config, dict): + return {k: config_copy(v) for k, v in config.items()} + elif isinstance(config, list): + return [config_copy(v) for v in config] + else: + return deepcopy(config) + + +def parse_command(params, key, default): + result = default + for _i, _v in enumerate(params): + if _v.split("=")[0].strip() == key: + result = _v[_v.index('=') + 1:].strip() + break + return result + + +if __name__ == '__main__': + params = deepcopy(sys.argv) + + # Get the defaults from default.yaml + with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "default.yaml error: {}".format(exc) + + # Load algorithm and env base configs + env_config = _get_config(params, "--env-config", "envs") + alg_config = _get_config(params, "--config", "algs") + # config_dict = {**config_dict, **env_config, **alg_config} + config_dict = recursive_dict_update(config_dict, env_config) + config_dict = recursive_dict_update(config_dict, alg_config) + + # now add all the config to sacred + ex.add_config(config_dict) + + # Save to disk by default for sacred + map_name = parse_command(params, "env_args.map_name", config_dict['env_args']['map_name']) + algo_name = parse_command(params, "name", config_dict['name']) + local_results_path = parse_command(params, "local_results_path", config_dict['local_results_path']) + file_obs_path = join(results_path, local_results_path, "sacred", map_name, algo_name) + + logger.info("Saving to FileStorageObserver in {}.".format(file_obs_path)) + ex.observers.append(FileStorageObserver.create(file_obs_path)) + + ex.run_commandline(params) + + # flush + sys.stdout.flush() diff --git a/results/sacred/5m_vs_6m/feudal/1/config.json b/results/sacred/5m_vs_6m/feudal/1/config.json new file mode 100644 index 0000000..6d7644d --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/1/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 707858439, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/1/cout.txt b/results/sacred/5m_vs_6m/feudal/1/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/1/metrics.json b/results/sacred/5m_vs_6m/feudal/1/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/1/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/1/run.json b/results/sacred/5m_vs_6m/feudal/1/run.json new file mode 100644 index 0000000..ee97619 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/1/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 209, in run_sequential\n learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\FeUdal_learner.py\", line 45, in __init__\n self.critic = FeudalCritic(args.state_dim, args)\n", + "NameError: name 'FeudalCritic' is not defined\n" + ], + "heartbeat": "2024-12-29T18:43:24.691551", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:43:20.056773", + "status": "FAILED", + "stop_time": "2024-12-29T18:43:24.693562" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/10/config.json b/results/sacred/5m_vs_6m/feudal/10/config.json new file mode 100644 index 0000000..4623928 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/10/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 507787181, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/10/cout.txt b/results/sacred/5m_vs_6m/feudal/10/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/10/metrics.json b/results/sacred/5m_vs_6m/feudal/10/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/10/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/10/run.json b/results/sacred/5m_vs_6m/feudal/10/run.json new file mode 100644 index 0000000..b4eac7c --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/10/run.json @@ -0,0 +1,131 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 120, in run\n actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated,\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 30, in select_actions\n agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 46, in forward\n worker_agent_outs, (new_worker_hidden, new_worker_cell), self.single_past_goals, self.batch_past_goals = self.worker_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 94, in forward\n hidden_state = hidden_state.to(device)\n", + "AttributeError: 'list' object has no attribute 'to'\n" + ], + "heartbeat": "2024-12-29T18:59:54.567302", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:59:31.491231", + "status": "FAILED", + "stop_time": "2024-12-29T18:59:54.569300" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/11/config.json b/results/sacred/5m_vs_6m/feudal/11/config.json new file mode 100644 index 0000000..928bf1a --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/11/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 181533407, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/11/cout.txt b/results/sacred/5m_vs_6m/feudal/11/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/11/metrics.json b/results/sacred/5m_vs_6m/feudal/11/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/11/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/11/run.json b/results/sacred/5m_vs_6m/feudal/11/run.json new file mode 100644 index 0000000..a98c1d0 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/11/run.json @@ -0,0 +1,131 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 120, in run\n actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated,\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 30, in select_actions\n agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 46, in forward\n worker_agent_outs, (new_worker_hidden, new_worker_cell), self.single_past_goals, self.batch_past_goals = self.worker_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 106, in forward\n h_in, c_in = hidden_state\n", + "ValueError: not enough values to unpack (expected 2, got 0)\n" + ], + "heartbeat": "2024-12-29T19:01:01.107233", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:00:37.074715", + "status": "FAILED", + "stop_time": "2024-12-29T19:01:01.109242" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/12/config.json b/results/sacred/5m_vs_6m/feudal/12/config.json new file mode 100644 index 0000000..882634f --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/12/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 437097629, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/12/cout.txt b/results/sacred/5m_vs_6m/feudal/12/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/12/info.json b/results/sacred/5m_vs_6m/feudal/12/info.json new file mode 100644 index 0000000..424bbe8 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/12/info.json @@ -0,0 +1,136 @@ +{ + "battle_won_mean": [ + 0.0 + ], + "battle_won_mean_T": [ + 150 + ], + "dead_allies_mean": [ + 4.75 + ], + "dead_allies_mean_T": [ + 150 + ], + "dead_enemies_mean": [ + 0.0 + ], + "dead_enemies_mean_T": [ + 150 + ], + "ep_length_mean": [ + 37.5 + ], + "ep_length_mean_T": [ + 150 + ], + "epsilon": [ + 1.0 + ], + "epsilon_T": [ + 150 + ], + "return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.5849056603773584 + } + ], + "return_max_T": [ + 150 + ], + "return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.2452830188679245 + } + ], + "return_mean_T": [ + 150 + ], + "return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.9056603773584906 + } + ], + "return_min_T": [ + 150 + ], + "return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.25313977103771196 + } + ], + "return_std_T": [ + 150 + ], + "test_battle_won_mean": [ + 0.0 + ], + "test_battle_won_mean_T": [ + 150 + ], + "test_dead_allies_mean": [ + 5.0 + ], + "test_dead_allies_mean_T": [ + 150 + ], + "test_dead_enemies_mean": [ + 0.0 + ], + "test_dead_enemies_mean_T": [ + 150 + ], + "test_ep_length_mean": [ + 20.78125 + ], + "test_ep_length_mean_T": [ + 150 + ], + "test_return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_max_T": [ + 150 + ], + "test_return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_mean_T": [ + 150 + ], + "test_return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_min_T": [ + 150 + ], + "test_return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_std_T": [ + 150 + ] +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/12/metrics.json b/results/sacred/5m_vs_6m/feudal/12/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/12/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/12/run.json b/results/sacred/5m_vs_6m/feudal/12/run.json new file mode 100644 index 0000000..863e000 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/12/run.json @@ -0,0 +1,132 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 280, in run_sequential\n learner.train(episode_sample, runner.t_env, episode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\FeUdal_learner.py\", line 60, in train\n agent_outs, goals, values = self.mac.forward(batch, t=t)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 39, in forward\n manager_goal, manager_value, (new_manager_hidden, new_manager_cell) = self.manager_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 29, in forward\n x = F.relu(self.manager_fc1(inputs))\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\linear.py\", line 114, in forward\n return F.linear(input, self.weight, self.bias)\n", + "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_addmm)\n" + ], + "heartbeat": "2024-12-29T19:02:57.737769", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": 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+ "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 371392846, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/13/cout.txt b/results/sacred/5m_vs_6m/feudal/13/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/13/metrics.json b/results/sacred/5m_vs_6m/feudal/13/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/13/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/13/run.json b/results/sacred/5m_vs_6m/feudal/13/run.json new file mode 100644 index 0000000..22c55a0 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/13/run.json 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4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:10:51.629933", + "status": "INTERRUPTED", + "stop_time": "2024-12-29T19:10:51.716375" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/14/config.json b/results/sacred/5m_vs_6m/feudal/14/config.json new file mode 100644 index 0000000..e9a7a29 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/14/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 32, + "batch_size_run": 1, + "buffer_cpu_only": true, + "buffer_size": 32, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 50000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "episode", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 466041091, + "state_dim": 80, + "t_max": 2050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/14/cout.txt b/results/sacred/5m_vs_6m/feudal/14/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/14/metrics.json b/results/sacred/5m_vs_6m/feudal/14/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/14/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/14/run.json b/results/sacred/5m_vs_6m/feudal/14/run.json new file mode 100644 index 0000000..a4ad6bb --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/14/run.json @@ -0,0 +1,114 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 161, in run_sequential\n env_info = runner.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\episode_runner.py\", line 47, in get_env_info\n return self.env.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\envs\\smac_v1\\StarCraft2EnvWrapper.py\", line 61, in get_env_info\n print(env_info)\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T19:11:26.734449", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "env_args": { + "map_name": "5m_vs_6m" + }, + "obs_agent_id": true, + "obs_last_action": true + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:11:26.237023", + "status": "FAILED", + "stop_time": "2024-12-29T19:11:26.736456" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/15/config.json b/results/sacred/5m_vs_6m/feudal/15/config.json new file mode 100644 index 0000000..eed5543 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/15/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 32, + "batch_size_run": 1, + "buffer_cpu_only": true, + "buffer_size": 32, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 50000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "episode", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 682299016, + "state_dim": 80, + "t_max": 2050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/15/cout.txt b/results/sacred/5m_vs_6m/feudal/15/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/15/metrics.json b/results/sacred/5m_vs_6m/feudal/15/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/15/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/15/run.json b/results/sacred/5m_vs_6m/feudal/15/run.json new file mode 100644 index 0000000..87af068 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/15/run.json @@ -0,0 +1,114 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 161, in run_sequential\n env_info = runner.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\episode_runner.py\", line 47, in get_env_info\n return self.env.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\envs\\smac_v1\\StarCraft2EnvWrapper.py\", line 42, in get_env_info\n print(\"\u958b\u59cb\u7372\u53d6\u74b0\u5883\u4fe1\u606f...\") # \u6dfb\u52a0\u8abf\u8a66\u4fe1\u606f\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T19:12:44.471688", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "env_args": { + "map_name": "5m_vs_6m" + }, + "obs_agent_id": true, + "obs_last_action": true + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:12:43.954086", + "status": "FAILED", + "stop_time": "2024-12-29T19:12:44.473189" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/16/config.json b/results/sacred/5m_vs_6m/feudal/16/config.json new file mode 100644 index 0000000..f4d8227 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/16/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 32, + "batch_size_run": 1, + "buffer_cpu_only": true, + "buffer_size": 32, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 50000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "episode", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 762805705, + "state_dim": 80, + "t_max": 2050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/16/cout.txt b/results/sacred/5m_vs_6m/feudal/16/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/16/metrics.json b/results/sacred/5m_vs_6m/feudal/16/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/16/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/16/run.json b/results/sacred/5m_vs_6m/feudal/16/run.json new file mode 100644 index 0000000..1663f7f --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/16/run.json @@ -0,0 +1,114 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 161, in run_sequential\n env_info = runner.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\episode_runner.py\", line 47, in get_env_info\n return self.env.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\envs\\smac_v1\\StarCraft2EnvWrapper.py\", line 42, in get_env_info\n print(\"Starting to get environment info...\") # \u6539\u7528\u82f1\u6587\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T19:14:25.404494", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "env_args": { + "map_name": "5m_vs_6m" + }, + "obs_agent_id": true, + "obs_last_action": true + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:14:24.916934", + "status": "FAILED", + "stop_time": "2024-12-29T19:14:25.405490" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/17/config.json b/results/sacred/5m_vs_6m/feudal/17/config.json new file mode 100644 index 0000000..0d9de35 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/17/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 32, + "batch_size_run": 1, + "buffer_cpu_only": true, + "buffer_size": 32, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 50000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "episode", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 106831448, + "state_dim": 80, + "t_max": 2050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/17/cout.txt b/results/sacred/5m_vs_6m/feudal/17/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/17/metrics.json b/results/sacred/5m_vs_6m/feudal/17/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/17/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/17/run.json b/results/sacred/5m_vs_6m/feudal/17/run.json new file mode 100644 index 0000000..440f00b --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/17/run.json @@ -0,0 +1,114 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 161, in run_sequential\n env_info = runner.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\episode_runner.py\", line 47, in get_env_info\n return self.env.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\envs\\smac_v1\\StarCraft2EnvWrapper.py\", line 42, in get_env_info\n print(\"Starting to get environment info...\")\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T19:18:05.729217", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "env_args": { + "map_name": "5m_vs_6m" + }, + "obs_agent_id": true, + "obs_last_action": true + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:18:05.247932", + "status": "FAILED", + "stop_time": "2024-12-29T19:18:05.731222" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/18/config.json b/results/sacred/5m_vs_6m/feudal/18/config.json new file mode 100644 index 0000000..76575ac --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/18/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 32, + "batch_size_run": 1, + "buffer_cpu_only": true, + "buffer_size": 32, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 50000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "episode", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 883123739, + "state_dim": 80, + "t_max": 2050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/18/cout.txt b/results/sacred/5m_vs_6m/feudal/18/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/18/metrics.json b/results/sacred/5m_vs_6m/feudal/18/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/18/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/18/run.json b/results/sacred/5m_vs_6m/feudal/18/run.json new file mode 100644 index 0000000..5a1cfec --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/18/run.json @@ -0,0 +1,114 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 161, in run_sequential\n env_info = runner.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\episode_runner.py\", line 47, in get_env_info\n return self.env.get_env_info()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\envs\\smac_v1\\StarCraft2EnvWrapper.py\", line 42, in get_env_info\n print(\"Starting to get environment info...\") # \u6539\u7528\u82f1\u6587\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T19:19:47.567781", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "env_args": { + "map_name": "5m_vs_6m" + }, + "obs_agent_id": true, + "obs_last_action": true + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:19:47.096681", + "status": "FAILED", + "stop_time": "2024-12-29T19:19:47.569280" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/2/config.json b/results/sacred/5m_vs_6m/feudal/2/config.json new file mode 100644 index 0000000..49e0dbc --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/2/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 499145602, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/2/cout.txt b/results/sacred/5m_vs_6m/feudal/2/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/2/metrics.json b/results/sacred/5m_vs_6m/feudal/2/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/2/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/2/run.json b/results/sacred/5m_vs_6m/feudal/2/run.json new file mode 100644 index 0000000..6022f05 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/2/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 209, in run_sequential\n learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\FeUdal_learner.py\", line 49, in __init__\n self.manager_params += list(self.critic.parameters())\n", + "AttributeError: 'FeudalLearner' object has no attribute 'critic'\n" + ], + "heartbeat": "2024-12-29T18:43:55.150259", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:43:50.734338", + "status": "FAILED", + "stop_time": "2024-12-29T18:43:55.151759" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/3/config.json b/results/sacred/5m_vs_6m/feudal/3/config.json new file mode 100644 index 0000000..22f3c27 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/3/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 517178064, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/3/cout.txt b/results/sacred/5m_vs_6m/feudal/3/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/3/metrics.json b/results/sacred/5m_vs_6m/feudal/3/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/3/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/3/run.json b/results/sacred/5m_vs_6m/feudal/3/run.json new file mode 100644 index 0000000..a6f3a4a --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/3/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 209, in run_sequential\n learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\FeUdal_learner.py\", line 52, in __init__\n self.critic_optimiser = RMSprop(params=self.critic.parameters(),\n", + "AttributeError: 'FeudalLearner' object has no attribute 'critic'\n" + ], + "heartbeat": "2024-12-29T18:44:14.125164", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:44:09.705383", + "status": "FAILED", + "stop_time": "2024-12-29T18:44:14.133193" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/4/config.json b/results/sacred/5m_vs_6m/feudal/4/config.json new file mode 100644 index 0000000..0bf0bcb --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/4/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 872351197, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/4/cout.txt b/results/sacred/5m_vs_6m/feudal/4/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/4/metrics.json b/results/sacred/5m_vs_6m/feudal/4/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/4/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/4/run.json b/results/sacred/5m_vs_6m/feudal/4/run.json new file mode 100644 index 0000000..edbaf43 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/4/run.json @@ -0,0 +1,128 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 102, in run\n self.reset()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 77, in reset\n if (self.args.use_cuda and self.args.cpu_inference) and str(self.mac.get_device()) != \"cpu\":\n", + "AttributeError: 'FeUdalMAC' object has no attribute 'get_device'\n" + ], + "heartbeat": "2024-12-29T18:46:09.443531", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:46:05.004412", + "status": "FAILED", + "stop_time": "2024-12-29T18:46:09.445530" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/5/config.json b/results/sacred/5m_vs_6m/feudal/5/config.json new file mode 100644 index 0000000..9ef4710 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/5/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + 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"feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 596105235, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/5/cout.txt b/results/sacred/5m_vs_6m/feudal/5/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/5/metrics.json b/results/sacred/5m_vs_6m/feudal/5/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/5/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/5/run.json b/results/sacred/5m_vs_6m/feudal/5/run.json new file mode 100644 index 0000000..8d69791 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/5/run.json @@ -0,0 +1,130 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 102, in run\n self.reset()\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 77, in reset\n if (self.args.use_cuda and self.args.cpu_inference) and str(self.mac.get_device()) != \"cpu\":\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 90, in get_device\n return next(self.parameters()).device\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 69, in parameters\n return self.manager_agent.parameters() + self.worker_agent.parameters()\n", + "TypeError: unsupported operand type(s) for +: 'generator' and 'generator'\n" + ], + "heartbeat": "2024-12-29T18:48:15.264543", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:48:10.740109", + "status": "FAILED", + "stop_time": "2024-12-29T18:48:15.266044" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/6/config.json b/results/sacred/5m_vs_6m/feudal/6/config.json new file mode 100644 index 0000000..107d563 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/6/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": 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"intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 204707139, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/6/cout.txt b/results/sacred/5m_vs_6m/feudal/6/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/6/info.json b/results/sacred/5m_vs_6m/feudal/6/info.json new file mode 100644 index 0000000..de3c993 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/6/info.json @@ -0,0 +1,136 @@ +{ + "battle_won_mean": [ + 0.0 + ], + "battle_won_mean_T": [ + 97 + ], + "dead_allies_mean": [ + 5.0 + ], + "dead_allies_mean_T": [ + 97 + ], + "dead_enemies_mean": [ + 0.0 + ], + "dead_enemies_mean_T": [ + 97 + ], + "ep_length_mean": [ + 24.25 + ], + "ep_length_mean_T": [ + 97 + ], + "epsilon": [ + 1.0 + ], + "epsilon_T": [ + 97 + ], + "return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.2641509433962264 + } + ], + "return_max_T": [ + 97 + ], + "return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.7547169811320753 + } + ], + "return_mean_T": [ + 97 + ], + "return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.3584905660377358 + } + ], + "return_min_T": [ + 97 + ], + "return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.33487244055280846 + } + ], + "return_std_T": [ + 97 + ], + "test_battle_won_mean": [ + 0.0 + ], + "test_battle_won_mean_T": [ + 97 + ], + "test_dead_allies_mean": [ + 0.0 + ], + "test_dead_allies_mean_T": [ + 97 + ], + "test_dead_enemies_mean": [ + 0.0 + ], + "test_dead_enemies_mean_T": [ + 97 + ], + "test_ep_length_mean": [ + 70.0 + ], + "test_ep_length_mean_T": [ + 97 + ], + "test_return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_max_T": [ + 97 + ], + "test_return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_mean_T": [ + 97 + ], + "test_return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_min_T": [ + 97 + ], + "test_return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_std_T": [ + 97 + ] +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/6/metrics.json b/results/sacred/5m_vs_6m/feudal/6/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/6/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/6/run.json b/results/sacred/5m_vs_6m/feudal/6/run.json new file mode 100644 index 0000000..2422191 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/6/run.json @@ -0,0 +1,132 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 280, in run_sequential\n learner.train(episode_sample, runner.t_env, episode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\FeUdal_learner.py\", line 60, in train\n agent_outs, goals, values = self.mac.forward(batch, t=t)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 39, in forward\n manager_goal, manager_value, (new_manager_hidden, new_manager_cell) = self.manager_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 29, in forward\n x = F.relu(self.manager_fc1(inputs))\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\linear.py\", line 114, in forward\n return F.linear(input, self.weight, self.bias)\n", + "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_addmm)\n" + ], + "heartbeat": "2024-12-29T18:49:53.258783", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:49:13.819758", + "status": "FAILED", + "stop_time": "2024-12-29T18:49:53.262287" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/7/config.json b/results/sacred/5m_vs_6m/feudal/7/config.json new file mode 100644 index 0000000..1d0ddb3 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/7/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 349608658, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/7/cout.txt b/results/sacred/5m_vs_6m/feudal/7/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/7/metrics.json b/results/sacred/5m_vs_6m/feudal/7/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/7/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/7/run.json b/results/sacred/5m_vs_6m/feudal/7/run.json new file mode 100644 index 0000000..ed25dd2 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/7/run.json @@ -0,0 +1,131 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 120, in run\n actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated,\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 30, in select_actions\n agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 39, in forward\n manager_goal, manager_value, (new_manager_hidden, new_manager_cell) = self.manager_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 33, in forward\n hidden = hidden.to(device)\n", + "AttributeError: 'tuple' object has no attribute 'to'\n" + ], + "heartbeat": "2024-12-29T18:51:18.974675", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:50:58.905345", + "status": "FAILED", + "stop_time": "2024-12-29T18:51:18.976696" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/8/config.json b/results/sacred/5m_vs_6m/feudal/8/config.json new file mode 100644 index 0000000..3923a6f --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/8/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 748911858, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/8/cout.txt b/results/sacred/5m_vs_6m/feudal/8/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/8/metrics.json b/results/sacred/5m_vs_6m/feudal/8/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/8/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/8/run.json b/results/sacred/5m_vs_6m/feudal/8/run.json new file mode 100644 index 0000000..9362c85 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/8/run.json @@ -0,0 +1,130 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 120, in run\n actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated,\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 30, in select_actions\n agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 46, in forward\n worker_agent_outs, (new_worker_hidden, new_worker_cell), self.single_past_goals, self.batch_past_goals = self.worker_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + "TypeError: Feudal_WorkerAgent.forward() takes from 3 to 5 positional arguments but 6 were given\n" + ], + "heartbeat": "2024-12-29T18:52:36.267587", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:52:12.335022", + "status": "FAILED", + "stop_time": "2024-12-29T18:52:36.268585" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/9/config.json b/results/sacred/5m_vs_6m/feudal/9/config.json new file mode 100644 index 0000000..b08c90d --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/9/config.json @@ -0,0 +1,97 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "feudal", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 4, + "buffer_cpu_only": true, + "buffer_size": 5000, + "c": 1, + "checkpoint_path": "", + "cpu_inference": true, + "critic_hidden_dim": 64, + "critic_lr": 0.0005, + "double_q": true, + "embedding_dim": 16, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gamma": 0.99, + "goal_dim": 16, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "hypernet_layers": 2, + "intrinsic_rewards_alpha": 0.001, + "label": "default_label", + "learner": "feudal_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.0005, + "mac": "feudal_mac", + "manager_hidden_dim": 64, + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "feudal", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "repeat_id": 1, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 859599929, + "state_dim": 80, + "t_max": 100000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_tensorboard": true, + "vf_coef": 0.5, + "worker_hidden_dim": 64 +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/9/cout.txt b/results/sacred/5m_vs_6m/feudal/9/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/feudal/9/metrics.json b/results/sacred/5m_vs_6m/feudal/9/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/9/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/9/run.json b/results/sacred/5m_vs_6m/feudal/9/run.json new file mode 100644 index 0000000..ecea6e3 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/9/run.json @@ -0,0 +1,131 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 261, in run_sequential\n episode_batch = runner.run(test_mode=False)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 120, in run\n actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated,\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 30, in select_actions\n agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\FeUdal_controller.py\", line 46, in forward\n worker_agent_outs, (new_worker_hidden, new_worker_cell), self.single_past_goals, self.batch_past_goals = self.worker_agent(\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 1194, in _call_impl\n return forward_call(*input, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\modules\\agents\\FeUdal_agent.py\", line 84, in forward\n goal = goal.to(device)\n", + "AttributeError: 'tuple' object has no attribute 'to'\n" + ], + "heartbeat": "2024-12-29T18:58:43.505662", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:58:22.158179", + "status": "FAILED", + "stop_time": "2024-12-29T18:58:43.507170" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py b/results/sacred/5m_vs_6m/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py new file mode 100644 index 0000000..5393b7f --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/_sources/logging_f71df6d788e929fac28afdf951d63d54.py @@ -0,0 +1,68 @@ +from collections import defaultdict +import logging +import numpy as np +import torch as th + +class Logger: + def __init__(self, console_logger): + self.console_logger = console_logger + + self.use_tb = False + self.use_sacred = False + self.use_hdf = False + + self.stats = defaultdict(lambda: []) + + def setup_tb(self, directory_name): + # Import here so it doesn't have to be installed if you don't use it + from tensorboard_logger import configure, log_value + configure(directory_name) + self.tb_logger = log_value + self.use_tb = True + + def setup_sacred(self, sacred_run_dict): + self.sacred_info = sacred_run_dict.info + self.use_sacred = True + + def log_stat(self, key, value, t, to_sacred=True): + self.stats[key].append((t, value)) + + if self.use_tb: + self.tb_logger(key, value, t) + + if self.use_sacred and to_sacred: + if key in self.sacred_info: + self.sacred_info["{}_T".format(key)].append(t) + self.sacred_info[key].append(value) + else: + self.sacred_info["{}_T".format(key)] = [t] + self.sacred_info[key] = [value] + + def print_recent_stats(self): + log_str = "Recent Stats | t_env: {:>10} | Episode: {:>8}\n".format(*self.stats["episode"][-1]) + i = 0 + for (k, v) in sorted(self.stats.items()): + if k == "episode": + continue + i += 1 + window = 5 if k != "epsilon" else 1 + item = "{:.4f}".format(th.mean(th.tensor([float(x[1]) for x in self.stats[k][-window:]]))) + log_str += "{:<25}{:>8}".format(k + ":", item) + log_str += "\n" if i % 4 == 0 else "\t" + self.console_logger.info(log_str) + # Reset stats to avoid accumulating logs in memory + self.stats = defaultdict(lambda: []) + + +# set up a custom logger +def get_logger(): + logger = logging.getLogger() + logger.handlers = [] + ch = logging.StreamHandler() + formatter = logging.Formatter('[%(levelname)s %(asctime)s] %(name)s %(message)s', '%H:%M:%S') + ch.setFormatter(formatter) + logger.addHandler(ch) + logger.setLevel('DEBUG') + + return logger + diff --git a/results/sacred/5m_vs_6m/feudal/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py b/results/sacred/5m_vs_6m/feudal/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py new file mode 100644 index 0000000..47de339 --- /dev/null +++ b/results/sacred/5m_vs_6m/feudal/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py @@ -0,0 +1,124 @@ +import random + +import numpy as np +import os +import collections +from os.path import dirname, abspath, join +from copy import deepcopy +from sacred import Experiment, SETTINGS +from sacred.observers import FileStorageObserver +from sacred.utils import apply_backspaces_and_linefeeds +import sys +import torch as th +from utils.logging import get_logger +import yaml +import collections.abc + +from run import REGISTRY as run_REGISTRY + +SETTINGS['CAPTURE_MODE'] = "fd" # set to "no" if you want to see stdout/stderr in console +logger = get_logger() + +ex = Experiment("pymarl") +ex.logger = logger +ex.captured_out_filter = apply_backspaces_and_linefeeds + +results_path = join(dirname(dirname(abspath(__file__)))) + + +@ex.main +def my_main(_run, _config, _log): + # Setting the random seed throughout the modules + config = config_copy(_config) + random.seed(config["seed"]) + np.random.seed(config["seed"]) + th.manual_seed(config["seed"]) + th.cuda.manual_seed(config["seed"]) + # th.cuda.manual_seed_all(config["seed"]) + th.backends.cudnn.deterministic = True # cudnn + + + config['env_args']['seed'] = config["seed"] + + # run + run_REGISTRY[_config['run']](_run, config, _log) + + +def _get_config(params, arg_name, subfolder): + config_name = None + for _i, _v in enumerate(params): + if _v.split("=")[0] == arg_name: + config_name = _v.split("=")[1] + del params[_i] + break + + if config_name is not None: + with open(os.path.join(os.path.dirname(__file__), "config", subfolder, "{}.yaml".format(config_name)), + "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "{}.yaml error: {}".format(config_name, exc) + return config_dict + + +def recursive_dict_update(d, u): + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = recursive_dict_update(d.get(k, {}), v) + else: + d[k] = v + return d + + +def config_copy(config): + if isinstance(config, dict): + return {k: config_copy(v) for k, v in config.items()} + elif isinstance(config, list): + return [config_copy(v) for v in config] + else: + return deepcopy(config) + + +def parse_command(params, key, default): + result = default + for _i, _v in enumerate(params): + if _v.split("=")[0].strip() == key: + result = _v[_v.index('=') + 1:].strip() + break + return result + + +if __name__ == '__main__': + params = deepcopy(sys.argv) + + # Get the defaults from default.yaml + with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "default.yaml error: {}".format(exc) + + # Load algorithm and env base configs + env_config = _get_config(params, "--env-config", "envs") + alg_config = _get_config(params, "--config", "algs") + # config_dict = {**config_dict, **env_config, **alg_config} + config_dict = recursive_dict_update(config_dict, env_config) + config_dict = recursive_dict_update(config_dict, alg_config) + + # now add all the config to sacred + ex.add_config(config_dict) + + # Save to disk by default for sacred + map_name = parse_command(params, "env_args.map_name", config_dict['env_args']['map_name']) + algo_name = parse_command(params, "name", config_dict['name']) + local_results_path = parse_command(params, "local_results_path", config_dict['local_results_path']) + file_obs_path = join(results_path, local_results_path, "sacred", map_name, algo_name) + + logger.info("Saving to FileStorageObserver in {}.".format(file_obs_path)) + ex.observers.append(FileStorageObserver.create(file_obs_path)) + + ex.run_commandline(params) + + # flush + sys.stdout.flush() diff --git a/results/sacred/5m_vs_6m/qmix/1/config.json b/results/sacred/5m_vs_6m/qmix/1/config.json new file mode 100644 index 0000000..c0e38d9 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/1/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 437873842, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 12, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/1/cout.txt b/results/sacred/5m_vs_6m/qmix/1/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/1/metrics.json b/results/sacred/5m_vs_6m/qmix/1/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/1/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/1/run.json b/results/sacred/5m_vs_6m/qmix/1/run.json new file mode 100644 index 0000000..1344538 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/1/run.json @@ -0,0 +1,129 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 203, in run_sequential\n mac = mac_REGISTRY[args.mac](buffer.scheme, groups, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\n_controller.py\", line 13, in __init__\n super(NMAC, self).__init__(scheme, groups, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\basic_controller.py\", line 15, in __init__\n self._build_agents(self.input_shape)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\controllers\\basic_controller.py\", line 83, in _build_agents\n print(\"&&&&&&&&&&&&&&&&&&&&&&\", self.args.agent, get_parameters_num(self.parameters()))\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:06:39.507641", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:06:32.973993", + "status": "FAILED", + "stop_time": "2024-12-29T18:06:39.509138" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/10/config.json b/results/sacred/5m_vs_6m/qmix/10/config.json new file mode 100644 index 0000000..f2e4be9 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/10/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 304770793, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/10/cout.txt b/results/sacred/5m_vs_6m/qmix/10/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/10/metrics.json b/results/sacred/5m_vs_6m/qmix/10/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/10/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/10/run.json b/results/sacred/5m_vs_6m/qmix/10/run.json new file mode 100644 index 0000000..22ce8ec --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/10/run.json @@ -0,0 +1,118 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": 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"INTERRUPTED", + "stop_time": "2024-12-29T19:16:35.379936" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/11/config.json b/results/sacred/5m_vs_6m/qmix/11/config.json new file mode 100644 index 0000000..e4e72ac --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/11/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + 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"optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 336442794, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/11/cout.txt b/results/sacred/5m_vs_6m/qmix/11/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/11/metrics.json b/results/sacred/5m_vs_6m/qmix/11/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/11/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/11/run.json b/results/sacred/5m_vs_6m/qmix/11/run.json new file mode 100644 index 0000000..7d0308e --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/11/run.json @@ -0,0 +1,104 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + 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"--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T19:17:02.366573", + "status": "INTERRUPTED", + "stop_time": "2024-12-29T19:17:57.522898" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/2/config.json b/results/sacred/5m_vs_6m/qmix/2/config.json new file mode 100644 index 0000000..9126a84 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/2/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 2441757, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 12, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/2/cout.txt b/results/sacred/5m_vs_6m/qmix/2/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/2/metrics.json b/results/sacred/5m_vs_6m/qmix/2/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/2/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/2/run.json b/results/sacred/5m_vs_6m/qmix/2/run.json new file mode 100644 index 0000000..f372ceb --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/2/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 206, in run_sequential\n runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\runners\\parallel_runner.py\", line 55, in setup\n print(\" &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& self.batch_device={}\".format(\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:07:57.434397", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:07:51.341620", + "status": "FAILED", + "stop_time": "2024-12-29T18:07:57.435895" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/3/config.json b/results/sacred/5m_vs_6m/qmix/3/config.json new file mode 100644 index 0000000..a2f026d --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/3/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 610338225, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 12, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/3/cout.txt b/results/sacred/5m_vs_6m/qmix/3/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/3/metrics.json b/results/sacred/5m_vs_6m/qmix/3/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/3/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/3/run.json b/results/sacred/5m_vs_6m/qmix/3/run.json new file mode 100644 index 0000000..155e9ff --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/3/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 209, in run_sequential\n learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\nq_learner.py\", line 75, in __init__\n print('Mixer Size: ')\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:08:50.856920", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:08:44.591867", + "status": "FAILED", + "stop_time": "2024-12-29T18:08:50.858921" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/4/config.json b/results/sacred/5m_vs_6m/qmix/4/config.json new file mode 100644 index 0000000..ebccf8c --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/4/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 133511293, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 12, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/4/cout.txt b/results/sacred/5m_vs_6m/qmix/4/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/4/info.json b/results/sacred/5m_vs_6m/qmix/4/info.json new file mode 100644 index 0000000..b630a3b --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/4/info.json @@ -0,0 +1,136 @@ +{ + "battle_won_mean": [ + 0.0 + ], + "battle_won_mean_T": [ + 211 + ], + "dead_allies_mean": [ + 5.0 + ], + "dead_allies_mean_T": [ + 211 + ], + "dead_enemies_mean": [ + 0.0 + ], + "dead_enemies_mean_T": [ + 211 + ], + "ep_length_mean": [ + 26.375 + ], + "ep_length_mean_T": [ + 211 + ], + "epsilon": [ + 1.0 + ], + "epsilon_T": [ + 211 + ], + "return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.3584905660377358 + } + ], + "return_max_T": [ + 211 + ], + "return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.2169811320754718 + } + ], + "return_mean_T": [ + 211 + ], + "return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.9056603773584906 + } + ], + "return_min_T": [ + 211 + ], + "return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.1575782366838685 + } + ], + "return_std_T": [ + 211 + ], + "test_battle_won_mean": [ + 0.0 + ], + "test_battle_won_mean_T": [ + 211 + ], + "test_dead_allies_mean": [ + 5.0 + ], + "test_dead_allies_mean_T": [ + 211 + ], + "test_dead_enemies_mean": [ + 1.0 + ], + "test_dead_enemies_mean_T": [ + 211 + ], + "test_ep_length_mean": [ + 18.09375 + ], + "test_ep_length_mean_T": [ + 211 + ], + "test_return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.5283018867924527 + } + ], + "test_return_max_T": [ + 211 + ], + "test_return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.1745283018867925 + } + ], + "test_return_mean_T": [ + 211 + ], + "test_return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.0754716981132075 + } + ], + "test_return_min_T": [ + 211 + ], + "test_return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.13792633506805135 + } + ], + "test_return_std_T": [ + 211 + ] +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/4/metrics.json b/results/sacred/5m_vs_6m/qmix/4/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/4/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/4/run.json b/results/sacred/5m_vs_6m/qmix/4/run.json new file mode 100644 index 0000000..3e8d626 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/4/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 280, in run_sequential\n learner.train(episode_sample, runner.t_env, episode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\nq_learner.py\", line 183, in train\n print(\"Avg cost {} seconds\".format(self.avg_time))\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:12:22.629336", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:11:28.868612", + "status": "FAILED", + "stop_time": "2024-12-29T18:12:22.633852" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/5/config.json b/results/sacred/5m_vs_6m/qmix/5/config.json new file mode 100644 index 0000000..67621f7 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/5/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 985419632, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 12, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/5/cout.txt b/results/sacred/5m_vs_6m/qmix/5/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/5/info.json b/results/sacred/5m_vs_6m/qmix/5/info.json new file mode 100644 index 0000000..20a4d19 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/5/info.json @@ -0,0 +1,136 @@ +{ + "battle_won_mean": [ + 0.0 + ], + "battle_won_mean_T": [ + 188 + ], + "dead_allies_mean": [ + 5.0 + ], + "dead_allies_mean_T": [ + 188 + ], + "dead_enemies_mean": [ + 0.0 + ], + "dead_enemies_mean_T": [ + 188 + ], + "ep_length_mean": [ + 23.5 + ], + "ep_length_mean_T": [ + 188 + ], + "epsilon": [ + 1.0 + ], + "epsilon_T": [ + 188 + ], + "return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.490566037735849 + } + ], + "return_max_T": [ + 188 + ], + "return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.4716981132075473 + } + ], + "return_mean_T": [ + 188 + ], + "return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.1320754716981132 + } + ], + "return_min_T": [ + 188 + ], + "return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.45283018867924524 + } + ], + "return_std_T": [ + 188 + ], + "test_battle_won_mean": [ + 0.0 + ], + "test_battle_won_mean_T": [ + 188 + ], + "test_dead_allies_mean": [ + 1.875 + ], + "test_dead_allies_mean_T": [ + 188 + ], + "test_dead_enemies_mean": [ + 0.0 + ], + "test_dead_enemies_mean_T": [ + 188 + ], + "test_ep_length_mean": [ + 68.84375 + ], + "test_ep_length_mean_T": [ + 188 + ], + "test_return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.1320754716981132 + } + ], + "test_return_max_T": [ + 188 + ], + "test_return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.4033018867924528 + } + ], + "test_return_mean_T": [ + 188 + ], + "test_return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.22641509433962265 + } + ], + "test_return_min_T": [ + 188 + ], + "test_return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.27576191419676993 + } + ], + "test_return_std_T": [ + 188 + ] +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/5/metrics.json b/results/sacred/5m_vs_6m/qmix/5/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/5/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/5/run.json b/results/sacred/5m_vs_6m/qmix/5/run.json new file mode 100644 index 0000000..f8831ed --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/5/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 280, in run_sequential\n learner.train(episode_sample, runner.t_env, episode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\nq_learner.py\", line 184, in train\n print('avg_time: ', self.avg_time)\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:15:38.197849", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:14:47.245762", + "status": "FAILED", + "stop_time": "2024-12-29T18:15:38.201847" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/6/config.json b/results/sacred/5m_vs_6m/qmix/6/config.json new file mode 100644 index 0000000..da55123 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/6/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 860875643, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/6/cout.txt b/results/sacred/5m_vs_6m/qmix/6/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/6/info.json b/results/sacred/5m_vs_6m/qmix/6/info.json new file mode 100644 index 0000000..fcdcf69 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/6/info.json @@ -0,0 +1,136 @@ +{ + "battle_won_mean": [ + 0.0 + ], + "battle_won_mean_T": [ + 196 + ], + "dead_allies_mean": [ + 5.0 + ], + "dead_allies_mean_T": [ + 196 + ], + "dead_enemies_mean": [ + 0.0 + ], + "dead_enemies_mean_T": [ + 196 + ], + "ep_length_mean": [ + 24.5 + ], + "ep_length_mean_T": [ + 196 + ], + "epsilon": [ + 1.0 + ], + "epsilon_T": [ + 196 + ], + "return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 2.2641509433962264 + } + ], + "return_max_T": [ + 196 + ], + "return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 1.2735849056603774 + } + ], + "return_mean_T": [ + 196 + ], + "return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.4528301886792453 + } + ], + "return_min_T": [ + 196 + ], + "return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.5421861526604155 + } + ], + "return_std_T": [ + 196 + ], + "test_battle_won_mean": [ + 0.0 + ], + "test_battle_won_mean_T": [ + 196 + ], + "test_dead_allies_mean": [ + 0.21875 + ], + "test_dead_allies_mean_T": [ + 196 + ], + "test_dead_enemies_mean": [ + 0.03125 + ], + "test_dead_enemies_mean_T": [ + 196 + ], + "test_ep_length_mean": [ + 68.875 + ], + "test_ep_length_mean_T": [ + 196 + ], + "test_return_max": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 3.433962264150943 + } + ], + "test_return_max_T": [ + 196 + ], + "test_return_mean": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.1497641509433962 + } + ], + "test_return_mean_T": [ + 196 + ], + "test_return_min": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.0 + } + ], + "test_return_min_T": [ + 196 + ], + "test_return_std": [ + { + "dtype": "float64", + "py/object": "numpy.float64", + "value": 0.612298627906809 + } + ], + "test_return_std_T": [ + 196 + ] +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/6/metrics.json b/results/sacred/5m_vs_6m/qmix/6/metrics.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/6/metrics.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/6/run.json b/results/sacred/5m_vs_6m/qmix/6/run.json new file mode 100644 index 0000000..f016f31 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/6/run.json @@ -0,0 +1,127 @@ +{ + "artifacts": [], + "command": "my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "fail_trace": [ + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Taiyo\\.conda\\envs\\SMACV2\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\main.py\", line 44, in my_main\n run_REGISTRY[_config['run']](_run, config, _log)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 280, in run_sequential\n learner.train(episode_sample, runner.t_env, episode)\n", + " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\learners\\nq_learner.py\", line 185, in train\n print(f\"Avg cost {self.avg_time} seconds\")\n", + "OSError: [WinError 1] \u529f\u80fd\u932f\u8aa4\u3002\n" + ], + "heartbeat": "2024-12-29T18:20:41.285841", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 8, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 10050000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=8", + "buffer_size=5000", + "t_max=10050000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:19:50.281614", + "status": "FAILED", + "stop_time": "2024-12-29T18:20:41.289345" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/7/config.json b/results/sacred/5m_vs_6m/qmix/7/config.json new file mode 100644 index 0000000..3ae44e8 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/7/config.json @@ -0,0 +1,96 @@ +{ + "action_selector": "epsilon_greedy", + "agent": "n_rnn", + "agent_output_type": "q", + "asn_hidden_size": 32, + "batch_size": 128, + "batch_size_run": 8, + "buffer_cpu_only": true, + "buffer_size": 5000, + "checkpoint_path": "", + "cpu_inference": true, + "critic_lr": 0.0005, + "double_q": true, + "enable_parallel_computing": false, + "env": "sc2", + "env_args": { + "continuing_episode": false, + "debug": false, + "difficulty": "7", + "game_version": null, + "heuristic_ai": false, + "heuristic_rest": false, + "map_name": "5m_vs_6m", + "move_amount": 2, + "obs_all_health": true, + "obs_instead_of_state": false, + "obs_last_action": false, + "obs_own_health": true, + "obs_pathing_grid": false, + "obs_terrain_height": false, + "obs_timestep_number": false, + "replay_dir": "", + "replay_prefix": "", + "reward_death_value": 10, + "reward_defeat": 0, + "reward_negative_scale": 0.5, + "reward_only_positive": true, + "reward_scale": true, + "reward_scale_rate": 20, + "reward_sparse": false, + "reward_win": 200, + "seed": null, + "state_last_action": true, + "state_timestep_number": false, + "step_mul": 8 + }, + "epsilon_anneal_time": 100000, + "epsilon_finish": 0.05, + "epsilon_start": 1.0, + "evaluate": false, + "gain": 0.01, + "gamma": 0.99, + "grad_norm_clip": 10, + "hypernet_embed": 64, + "label": "default_label", + "learner": "nq_learner", + "learner_log_interval": 10000, + "load_step": 0, + "local_results_path": "results", + "log_interval": 10000, + "lr": 0.001, + "mac": "n_mac", + "mixer": "qmix", + "mixing_embed_dim": 32, + "name": "qmix", + "obs_agent_id": true, + "obs_last_action": true, + "optim_alpha": 0.99, + "optim_eps": 1e-05, + "optimizer": "adam", + "per_alpha": 0.6, + "per_beta": 0.4, + "q_lambda": false, + "repeat_id": 1, + "return_priority": false, + "rnn_hidden_dim": 64, + "run": "default", + "runner": "parallel", + "runner_log_interval": 10000, + "save_model": true, + "save_model_interval": 2000000, + "save_replay": false, + "seed": 124389269, + "t_max": 10050000, + "target_update_interval": 200, + "td_lambda": 0.6, + "test_greedy": true, + "test_interval": 10000, + "test_nepisode": 32, + "thread_num": 4, + "use_cuda": true, + "use_layer_norm": false, + "use_orthogonal": false, + "use_per": false, + "use_tensorboard": true +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/7/cout.txt b/results/sacred/5m_vs_6m/qmix/7/cout.txt new file mode 100644 index 0000000..e69de29 diff --git a/results/sacred/5m_vs_6m/qmix/7/info.json b/results/sacred/5m_vs_6m/qmix/7/info.json new file mode 100644 index 0000000..915580e --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/7/info.json @@ -0,0 +1,1525 @@ +{ + "battle_won_mean": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "battle_won_mean_T": [ + 197, + 10276, + 20389, + 30510, + 40521, + 50534, + 60601, + 70713, + 80804, + 90808, + 100902, + 110949 + ], + "dead_allies_mean": [ + 5.0, + 4.99234693877551, + 4.997685185185185, + 4.997881355932203, + 4.996031746031746, + 5.0, + 5.0, + 5.0, + 4.998076923076923, + 4.979166666666667, + 5.0, + 5.0 + ], + "dead_allies_mean_T": [ + 197, + 10276, + 20389, + 30510, + 40521, + 50534, + 60601, + 70713, + 80804, + 90808, + 100902, + 110949 + ], + "dead_enemies_mean": [ + 0.0, + 0.002551020408163265, + 0.0, + 0.019067796610169493, + 0.07936507936507936, + 0.20522388059701493, + 0.5441176470588235, + 0.9253731343283582, + 1.3673076923076923, + 1.5520833333333333, + 2.0705645161290325, + 2.2172131147540983 + ], + "dead_enemies_mean_T": [ + 197, + 10276, + 20389, + 30510, + 40521, + 50534, + 60601, + 70713, + 80804, + 90808, + 100902, + 110949 + ], + "ep_length_mean": [ + 24.625, + 25.711734693877553, + 23.40972222222222, + 21.44279661016949, + 19.863095238095237, + 18.68097014925373, + 18.50551470588235, + 18.865671641791046, + 19.40576923076923, + 20.841666666666665, + 20.350806451612904, + 20.58811475409836 + ], + "ep_length_mean_T": [ + 197, + 10276, + 20389, + 30510, + 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"my_main", + "experiment": { + "base_dir": "C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src", + "dependencies": [ + "numpy==1.23.1", + "PyYAML==6.0.2", + "sacred==0.8.7", + "torch==1.13.1+cu117" + ], + "mainfile": "main.py", + "name": "pymarl", + "repositories": [ + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + }, + { + "commit": "44bb188185fd0292a1a306c86396027efb44224d", + "dirty": true, + "url": "https://github.com/tjuHaoXiaotian/pymarl3.git" + } + ], + "sources": [ + [ + "main.py", + "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py" + ], + [ + "utils\\logging.py", + "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py" + ] + ] + }, + "heartbeat": "2024-12-29T18:35:59.537230", + "host": { + "ENV": {}, + "cpu": "AMD Ryzen 7 5700X3D 8-Core Processor", + "gpus": { + "driver_version": "560.94", + "gpus": [ + { + "model": "NVIDIA GeForce RTX 4080 SUPER", + "persistence_mode": false, + "total_memory": 16376 + } + ] + }, + "hostname": "Taiyopen", + "os": [ + "Windows", + "Windows-10-10.0.22631-SP0" + ], + "python_version": "3.10.16" + }, + "meta": { + "command": "my_main", + "config_updates": { + "batch_size": 128, + "batch_size_run": 4, + "buffer_size": 5000, + "env_args": { + "map_name": "5m_vs_6m" + }, + "epsilon_anneal_time": 100000, + "obs_agent_id": true, + "obs_last_action": true, + "runner": "parallel", + "t_max": 100000, + "td_lambda": 0.6 + }, + "named_configs": [], + "options": { + "--beat-interval": null, + "--capture": null, + "--comment": null, + "--debug": false, + "--enforce_clean": false, + "--file_storage": null, + "--force": false, + "--help": false, + "--id": null, + "--loglevel": null, + "--mongo_db": null, + "--name": null, + "--pdb": false, + "--print-config": false, + "--priority": null, + "--queue": false, + "--s3": null, + "--sql": null, + "--tiny_db": null, + "--unobserved": false, + "COMMAND": null, + "UPDATE": [ + "env_args.map_name=5m_vs_6m", + "obs_agent_id=True", + "obs_last_action=True", + "runner=parallel", + "batch_size_run=4", + "buffer_size=5000", + "t_max=100000", + "epsilon_anneal_time=100000", + "batch_size=128", + "td_lambda=0.6" + ], + "help": false, + "with": true + } + }, + "resources": [], + "result": null, + "start_time": "2024-12-29T18:34:18.151259", + "status": "INTERRUPTED", + "stop_time": "2024-12-29T18:35:59.543818" +} \ No newline at end of file diff --git a/results/sacred/5m_vs_6m/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py b/results/sacred/5m_vs_6m/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py new file mode 100644 index 0000000..5393b7f --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/_sources/logging_f71df6d788e929fac28afdf951d63d54.py @@ -0,0 +1,68 @@ +from collections import defaultdict +import logging +import numpy as np +import torch as th + +class Logger: + def __init__(self, console_logger): + self.console_logger = console_logger + + self.use_tb = False + self.use_sacred = False + self.use_hdf = False + + self.stats = defaultdict(lambda: []) + + def setup_tb(self, directory_name): + # Import here so it doesn't have to be installed if you don't use it + from tensorboard_logger import configure, log_value + configure(directory_name) + self.tb_logger = log_value + self.use_tb = True + + def setup_sacred(self, sacred_run_dict): + self.sacred_info = sacred_run_dict.info + self.use_sacred = True + + def log_stat(self, key, value, t, to_sacred=True): + self.stats[key].append((t, value)) + + if self.use_tb: + self.tb_logger(key, value, t) + + if self.use_sacred and to_sacred: + if key in self.sacred_info: + self.sacred_info["{}_T".format(key)].append(t) + self.sacred_info[key].append(value) + else: + self.sacred_info["{}_T".format(key)] = [t] + self.sacred_info[key] = [value] + + def print_recent_stats(self): + log_str = "Recent Stats | t_env: {:>10} | Episode: {:>8}\n".format(*self.stats["episode"][-1]) + i = 0 + for (k, v) in sorted(self.stats.items()): + if k == "episode": + continue + i += 1 + window = 5 if k != "epsilon" else 1 + item = "{:.4f}".format(th.mean(th.tensor([float(x[1]) for x in self.stats[k][-window:]]))) + log_str += "{:<25}{:>8}".format(k + ":", item) + log_str += "\n" if i % 4 == 0 else "\t" + self.console_logger.info(log_str) + # Reset stats to avoid accumulating logs in memory + self.stats = defaultdict(lambda: []) + + +# set up a custom logger +def get_logger(): + logger = logging.getLogger() + logger.handlers = [] + ch = logging.StreamHandler() + formatter = logging.Formatter('[%(levelname)s %(asctime)s] %(name)s %(message)s', '%H:%M:%S') + ch.setFormatter(formatter) + logger.addHandler(ch) + logger.setLevel('DEBUG') + + return logger + diff --git a/results/sacred/5m_vs_6m/qmix/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py b/results/sacred/5m_vs_6m/qmix/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py new file mode 100644 index 0000000..47de339 --- /dev/null +++ b/results/sacred/5m_vs_6m/qmix/_sources/main_888918ff84cf3bc1e6e9dcc8919870b2.py @@ -0,0 +1,124 @@ +import random + +import numpy as np +import os +import collections +from os.path import dirname, abspath, join +from copy import deepcopy +from sacred import Experiment, SETTINGS +from sacred.observers import FileStorageObserver +from sacred.utils import apply_backspaces_and_linefeeds +import sys +import torch as th +from utils.logging import get_logger +import yaml +import collections.abc + +from run import REGISTRY as run_REGISTRY + +SETTINGS['CAPTURE_MODE'] = "fd" # set to "no" if you want to see stdout/stderr in console +logger = get_logger() + +ex = Experiment("pymarl") +ex.logger = logger +ex.captured_out_filter = apply_backspaces_and_linefeeds + +results_path = join(dirname(dirname(abspath(__file__)))) + + +@ex.main +def my_main(_run, _config, _log): + # Setting the random seed throughout the modules + config = config_copy(_config) + random.seed(config["seed"]) + np.random.seed(config["seed"]) + th.manual_seed(config["seed"]) + th.cuda.manual_seed(config["seed"]) + # th.cuda.manual_seed_all(config["seed"]) + th.backends.cudnn.deterministic = True # cudnn + + + config['env_args']['seed'] = config["seed"] + + # run + run_REGISTRY[_config['run']](_run, config, _log) + + +def _get_config(params, arg_name, subfolder): + config_name = None + for _i, _v in enumerate(params): + if _v.split("=")[0] == arg_name: + config_name = _v.split("=")[1] + del params[_i] + break + + if config_name is not None: + with open(os.path.join(os.path.dirname(__file__), "config", subfolder, "{}.yaml".format(config_name)), + "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "{}.yaml error: {}".format(config_name, exc) + return config_dict + + +def recursive_dict_update(d, u): + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = recursive_dict_update(d.get(k, {}), v) + else: + d[k] = v + return d + + +def config_copy(config): + if isinstance(config, dict): + return {k: config_copy(v) for k, v in config.items()} + elif isinstance(config, list): + return [config_copy(v) for v in config] + else: + return deepcopy(config) + + +def parse_command(params, key, default): + result = default + for _i, _v in enumerate(params): + if _v.split("=")[0].strip() == key: + result = _v[_v.index('=') + 1:].strip() + break + return result + + +if __name__ == '__main__': + params = deepcopy(sys.argv) + + # Get the defaults from default.yaml + with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "default.yaml error: {}".format(exc) + + # Load algorithm and env base configs + env_config = _get_config(params, "--env-config", "envs") + alg_config = _get_config(params, "--config", "algs") + # config_dict = {**config_dict, **env_config, **alg_config} 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b/src/components/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/components/action_selectors.py b/src/components/action_selectors.py new file mode 100644 index 0000000..33ee935 --- /dev/null +++ b/src/components/action_selectors.py @@ -0,0 +1,185 @@ +import torch as th +from torch.distributions import Categorical +from torch.distributions.one_hot_categorical import OneHotCategorical + +from .epsilon_schedules import DecayThenFlatSchedule + + +class GumbelSoftmax(OneHotCategorical): + + def __init__(self, logits, probs=None, temperature=1): + super(GumbelSoftmax, self).__init__(logits=logits, probs=probs) + self.eps = 1e-20 + self.temperature = temperature + + def sample_gumbel(self): + U = self.logits.clone() + U.uniform_(0, 1) + return -th.log(-th.log(U + self.eps)) + + def gumbel_softmax_sample(self): + y = self.logits + self.sample_gumbel() + return th.softmax(y / self.temperature, dim=-1) + + def hard_gumbel_softmax_sample(self): + y = self.gumbel_softmax_sample() + return (th.max(y, dim=-1, keepdim=True)[0] == y).float() + + def rsample(self): + return self.gumbel_softmax_sample() + + def sample(self): + return self.rsample().detach() + + def hard_sample(self): + return self.hard_gumbel_softmax_sample() + + +def multinomial_entropy(logits): + assert logits.size(-1) > 1 + return GumbelSoftmax(logits=logits).entropy() + + +REGISTRY = {} + + +class GumbelSoftmaxMultinomialActionSelector(): + + def __init__(self, args): + self.args = args + + self.schedule = DecayThenFlatSchedule(args.epsilon_start, args.epsilon_finish, args.epsilon_anneal_time, + decay="linear") + self.epsilon = self.schedule.eval(0) + self.test_greedy = getattr(args, "test_greedy", True) + self.save_probs = getattr(self.args, 'save_probs', False) + + def select_action(self, agent_logits, avail_actions, t_env, test_mode=False): + masked_policies = agent_logits.clone() + self.epsilon = self.schedule.eval(t_env) + + if test_mode and self.test_greedy: + picked_actions = masked_policies.max(dim=2)[1] + else: + picked_actions = GumbelSoftmax(logits=masked_policies).sample() + picked_actions = th.argmax(picked_actions, dim=-1).long() + + if self.save_probs: + return picked_actions, masked_policies + else: + return picked_actions + + +REGISTRY["gumbel"] = GumbelSoftmaxMultinomialActionSelector + + +class MultinomialActionSelector(): + + def __init__(self, args): + self.args = args + + self.schedule = DecayThenFlatSchedule(args.epsilon_start, args.epsilon_finish, args.epsilon_anneal_time, + decay="linear") + self.epsilon = self.schedule.eval(0) + + self.test_greedy = getattr(args, "test_greedy", True) + self.save_probs = getattr(self.args, 'save_probs', False) + + def select_action(self, agent_inputs, avail_actions, t_env, test_mode=False): + masked_policies = agent_inputs.clone() + masked_policies[avail_actions == 0] = 0 + masked_policies = masked_policies / (masked_policies.sum(-1, keepdim=True) + 1e-8) + + if test_mode and self.test_greedy: + picked_actions = masked_policies.max(dim=2)[1] + else: + self.epsilon = self.schedule.eval(t_env) + + epsilon_action_num = (avail_actions.sum(-1, keepdim=True) + 1e-8) + masked_policies = ((1 - self.epsilon) * masked_policies + + avail_actions * self.epsilon / epsilon_action_num) + masked_policies[avail_actions == 0] = 0 + + picked_actions = Categorical(masked_policies).sample().long() + + if self.save_probs: + return picked_actions, masked_policies + else: + return picked_actions + + +REGISTRY["multinomial"] = MultinomialActionSelector + + +def categorical_entropy(probs): + assert probs.size(-1) > 1 + return Categorical(probs=probs).entropy() + + +class EpsilonGreedyActionSelector(): + + def __init__(self, args): + self.args = args + + self.schedule = DecayThenFlatSchedule(args.epsilon_start, args.epsilon_finish, args.epsilon_anneal_time, + decay="linear") + self.epsilon = self.schedule.eval(0) + + def select_action(self, agent_inputs, avail_actions, t_env, test_mode=False): + # Assuming agent_inputs is a batch of Q-Values for each agent bav + self.epsilon = self.schedule.eval(t_env) + + if test_mode: + # Greedy action selection only + self.epsilon = getattr(self.args, "test_noise", 0.0) + + # mask actions that are excluded from selection + masked_q_values = agent_inputs.clone() + masked_q_values[avail_actions == 0] = -float("inf") # should never be selected! + + # random_numbers = th.rand_like(agent_inputs[:, :, 0]) # TODO: 为啥GPU和CPU model inference结果不同 + random_numbers = th.rand(size=agent_inputs[:, :, 0].size(), dtype=th.float32, device="cpu").to( + agent_inputs.device) + + pick_random = (random_numbers < self.epsilon).long() + # random_actions = Categorical(avail_actions.float()).sample().long() + random_actions = Categorical(avail_actions.cpu().float()).sample().long().to(avail_actions.device) + + picked_actions = pick_random * random_actions + (1 - pick_random) * masked_q_values.max(dim=2)[1] + return picked_actions + + +REGISTRY["epsilon_greedy"] = EpsilonGreedyActionSelector + + +class GaussianActionSelector(): + + def __init__(self, args): + self.args = args + self.test_greedy = getattr(args, "test_greedy", True) + + def select_action(self, mu, sigma, test_mode=False): + # Expects the following input dimensions: + # mu: [b x a x u] + # sigma: [b x a x u x u] + assert mu.dim() == 3, "incorrect input dim: mu" + assert sigma.dim() == 3, "incorrect input dim: sigma" + sigma = sigma.view(-1, self.args.n_agents, self.args.n_actions, self.args.n_actions) + + if test_mode and self.test_greedy: + picked_actions = mu + else: + dst = th.distributions.MultivariateNormal(mu.view(-1, + mu.shape[-1]), + sigma.view(-1, + mu.shape[-1], + mu.shape[-1])) + try: + picked_actions = dst.sample().view(*mu.shape) + except Exception as e: + a = 5 + pass + return picked_actions + + +REGISTRY["gaussian"] = GaussianActionSelector diff --git a/src/components/episode_buffer.py b/src/components/episode_buffer.py new file mode 100644 index 0000000..584d0a8 --- /dev/null +++ b/src/components/episode_buffer.py @@ -0,0 +1,361 @@ +import random +from types import SimpleNamespace as SN + +import numpy as np +import torch as th + +from .segment_tree import SumSegmentTree, MinSegmentTree + + +class EpisodeBatch: + def __init__(self, + scheme, + groups, + batch_size, + max_seq_length, + data=None, + preprocess=None, + device="cpu"): + self.scheme = scheme.copy() + self.groups = groups + self.batch_size = batch_size + self.max_seq_length = max_seq_length + self.preprocess = {} if preprocess is None else preprocess + self.device = device + + if data is not None: + self.data = data + else: + self.data = SN() + self.data.transition_data = {} + self.data.episode_data = {} + self._setup_data(self.scheme, self.groups, batch_size, max_seq_length, self.preprocess) + + def _setup_data(self, scheme, groups, batch_size, max_seq_length, preprocess): + if preprocess is not None: + for k in preprocess: + assert k in scheme + new_k = preprocess[k][0] + transforms = preprocess[k][1] + + vshape = self.scheme[k]["vshape"] + dtype = self.scheme[k]["dtype"] + for transform in transforms: + vshape, dtype = transform.infer_output_info(vshape, dtype) + + self.scheme[new_k] = { + "vshape": vshape, + "dtype": dtype + } + if "group" in self.scheme[k]: + self.scheme[new_k]["group"] = self.scheme[k]["group"] + if "episode_const" in self.scheme[k]: + self.scheme[new_k]["episode_const"] = self.scheme[k]["episode_const"] + + assert "filled" not in scheme, '"filled" is a reserved key for masking.' + scheme.update({ + "filled": {"vshape": (1,), "dtype": th.long}, + }) + + for field_key, field_info in scheme.items(): + assert "vshape" in field_info, "Scheme must define vshape for {}".format(field_key) + vshape = field_info["vshape"] + episode_const = field_info.get("episode_const", False) + group = field_info.get("group", None) + dtype = field_info.get("dtype", th.float32) + + if isinstance(vshape, int): + vshape = (vshape,) + + if group: + assert group in groups, "Group {} must have its number of members defined in _groups_".format(group) + shape = (groups[group], *vshape) + else: + shape = vshape + + if episode_const: + self.data.episode_data[field_key] = th.zeros((batch_size, *shape), dtype=dtype, device=self.device) + else: + self.data.transition_data[field_key] = th.zeros((batch_size, max_seq_length, *shape), dtype=dtype, + device=self.device) + + def extend(self, scheme, groups=None): + raise NotImplementedError + self._setup_data(scheme, self.groups if groups is None else groups, self.batch_size, self.max_seq_length) + + def to(self, device): + for k, v in self.data.transition_data.items(): + self.data.transition_data[k] = v.to(device) + for k, v in self.data.episode_data.items(): + self.data.episode_data[k] = v.to(device) + self.device = device + + def update(self, data, bs=slice(None), ts=slice(None), mark_filled=True): + slices = self._parse_slices((bs, ts)) + for k, v in data.items(): + if k in self.data.transition_data: + target = self.data.transition_data + if mark_filled: + target["filled"][slices] = 1 + mark_filled = False + _slices = slices + elif k in self.data.episode_data: + target = self.data.episode_data + _slices = slices[0] + else: + raise KeyError("{} not found in transition or episode data".format(k)) + + dtype = self.scheme[k].get("dtype", th.float32) + v = th.tensor(v, dtype=dtype, device=self.device) + self._check_safe_view(k, v, target[k][_slices]) + target[k][_slices] = v.view_as(target[k][_slices]) + + if k in self.preprocess: + new_k = self.preprocess[k][0] + v = target[k][_slices] + for transform in self.preprocess[k][1]: + v = transform.transform(v) + target[new_k][_slices] = v.view_as(target[new_k][_slices]) + + def _check_safe_view(self, k, v, dest): + idx = len(v.shape) - 1 + for s in dest.shape[::-1]: + if v.shape[idx] != s: + if s != 1: + raise ValueError("{}: unsafe reshape of {} to {}".format(k, v.shape, dest.shape)) + else: + idx -= 1 + + def __getitem__(self, item): + if isinstance(item, str): + if item in self.data.episode_data: + return self.data.episode_data[item] + elif item in self.data.transition_data: + return self.data.transition_data[item] + else: + raise ValueError + elif isinstance(item, tuple) and all([isinstance(it, str) for it in item]): + new_data = self._new_data_sn() + for key in item: + if key in self.data.transition_data: + new_data.transition_data[key] = self.data.transition_data[key] + elif key in self.data.episode_data: + new_data.episode_data[key] = self.data.episode_data[key] + else: + raise KeyError("Unrecognised key {}".format(key)) + + # Update the scheme to only have the requested keys + new_scheme = {key: self.scheme[key] for key in item} + new_groups = {self.scheme[key]["group"]: self.groups[self.scheme[key]["group"]] + for key in item if "group" in self.scheme[key]} + ret = EpisodeBatch(new_scheme, new_groups, self.batch_size, self.max_seq_length, data=new_data, + device=self.device) + return ret + else: + item = self._parse_slices(item) + new_data = self._new_data_sn() + for k, v in self.data.transition_data.items(): + new_data.transition_data[k] = v[item] + for k, v in self.data.episode_data.items(): + new_data.episode_data[k] = v[item[0]] + + ret_bs = self._get_num_items(item[0], self.batch_size) + ret_max_t = self._get_num_items(item[1], self.max_seq_length) + + ret = EpisodeBatch(self.scheme, self.groups, ret_bs, ret_max_t, data=new_data, device=self.device) + return ret + + def _get_num_items(self, indexing_item, max_size): + if isinstance(indexing_item, list) or isinstance(indexing_item, np.ndarray): + return len(indexing_item) + elif isinstance(indexing_item, slice): + _range = indexing_item.indices(max_size) + return 1 + (_range[1] - _range[0] - 1) // _range[2] + + def _new_data_sn(self): + new_data = SN() + new_data.transition_data = {} + new_data.episode_data = {} + return new_data + + def _parse_slices(self, items): + parsed = [] + # Only batch slice given, add full time slice + if (isinstance(items, slice) # slice a:b + or isinstance(items, int) # int i + or (isinstance(items, (list, np.ndarray, th.LongTensor, th.cuda.LongTensor))) # [a,b,c] + ): + items = (items, slice(None)) + + # Need the time indexing to be contiguous + if isinstance(items[1], list): + raise IndexError("Indexing across Time must be contiguous") + + for item in items: + # TODO: stronger checks to ensure only supported options get through + if isinstance(item, int): + # Convert single indices to slices + parsed.append(slice(item, item + 1)) + else: + # Leave slices and lists as is + parsed.append(item) + return parsed + + def max_t_filled(self): + return th.sum(self.data.transition_data["filled"], 1).max(0)[0] + + def __repr__(self): + return "EpisodeBatch. Batch Size:{} Max_seq_len:{} Keys:{} Groups:{}".format(self.batch_size, + self.max_seq_length, + self.scheme.keys(), + self.groups.keys()) + + def split(self, split_num): + if split_num == 1: + return [self] + split_data = [] + batch_size = self.batch_size // split_num + for i in range(split_num): + start_idx = i * batch_size + split_data.append(self[start_idx: start_idx + batch_size if i != split_num - 1 else self.batch_size]) + return split_data + + +class ReplayBuffer(EpisodeBatch): + def __init__(self, scheme, groups, buffer_size, max_seq_length, preprocess=None, device="cpu"): + super(ReplayBuffer, self).__init__(scheme, groups, buffer_size, max_seq_length, preprocess=preprocess, + device=device) + self.buffer_size = buffer_size # same as self.batch_size but more explicit + self.buffer_index = 0 + self.episodes_in_buffer = 0 + + def insert_episode_batch(self, ep_batch): + if self.buffer_index + ep_batch.batch_size <= self.buffer_size: + self.update(ep_batch.data.transition_data, + slice(self.buffer_index, self.buffer_index + ep_batch.batch_size), + slice(0, ep_batch.max_seq_length), + mark_filled=False) + self.update(ep_batch.data.episode_data, + slice(self.buffer_index, self.buffer_index + ep_batch.batch_size)) + self.buffer_index = (self.buffer_index + ep_batch.batch_size) + self.episodes_in_buffer = max(self.episodes_in_buffer, self.buffer_index) + self.buffer_index = self.buffer_index % self.buffer_size + assert self.buffer_index < self.buffer_size + else: + buffer_left = self.buffer_size - self.buffer_index + self.insert_episode_batch(ep_batch[0:buffer_left, :]) + self.insert_episode_batch(ep_batch[buffer_left:, :]) + + def can_sample(self, batch_size): + return self.episodes_in_buffer >= batch_size + + def sample(self, batch_size): + assert self.can_sample(batch_size) + if self.episodes_in_buffer == batch_size: + return self[:batch_size] + else: + # Uniform sampling only atm + ep_ids = np.random.choice(self.episodes_in_buffer, batch_size, replace=False) + return self[ep_ids] + + def uni_sample(self, batch_size): + return self.sample(batch_size) + + def sample_latest(self, batch_size): + assert self.can_sample(batch_size) + if self.buffer_index - batch_size < 0: + # Uniform sampling + return self.uni_sample(batch_size) + else: + # Return the latest + return self[self.buffer_index - batch_size: self.buffer_index] + + def __repr__(self): + return "ReplayBuffer. {}/{} episodes. Keys:{} Groups:{}".format(self.episodes_in_buffer, + self.buffer_size, + self.scheme.keys(), + self.groups.keys()) + + +# Adapted from the OpenAI Baseline implementations (https://github.com/openai/baselines) +class PrioritizedReplayBuffer(ReplayBuffer): + def __init__(self, scheme, groups, buffer_size, max_seq_length, alpha, beta, t_max, preprocess=None, device="cpu"): + super(PrioritizedReplayBuffer, self).__init__(scheme, groups, buffer_size, max_seq_length, + preprocess=preprocess, device="cpu") + self.alpha = alpha + self.beta_original = beta + self.beta = beta + self.beta_increment = (1.0 - beta) / t_max + self.max_priority = 1.0 + + it_capacity = 1 + while it_capacity < buffer_size: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + + def insert_episode_batch(self, ep_batch): + # TODO: convert batch/episode to idx? + pre_idx = self.buffer_index + super().insert_episode_batch(ep_batch) + idx = self.buffer_index + if idx >= pre_idx: + for i in range(idx - pre_idx): + self._it_sum[pre_idx + i] = self.max_priority ** self.alpha + self._it_min[pre_idx + i] = self.max_priority ** self.alpha + else: + for i in range(self.buffer_size - pre_idx): + self._it_sum[pre_idx + i] = self.max_priority ** self.alpha + self._it_min[pre_idx + i] = self.max_priority ** self.alpha + for i in range(self.buffer_index): + self._it_sum[i] = self.max_priority ** self.alpha + self._it_min[i] = self.max_priority ** self.alpha + + def _sample_proportional(self, batch_size): + res = [] + p_total = self._it_sum.sum(0, self.episodes_in_buffer - 1) + every_range_len = p_total / batch_size + for i in range(batch_size): + mass = random.random() * every_range_len + i * every_range_len + idx = self._it_sum.find_prefixsum_idx(mass) + res.append(idx) + return res + + def sample(self, batch_size, t): + assert self.can_sample(batch_size) + self.beta = self.beta_original + (t * self.beta_increment) + + idxes = self._sample_proportional(batch_size) + weights = [] + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * self.episodes_in_buffer) ** (-self.beta) + + for idx in idxes: + p_sample = self._it_sum[idx] / self._it_sum.sum() + weight = (p_sample * self.episodes_in_buffer) ** (-self.beta) + weights.append(weight / max_weight) + weights = np.array(weights) + + return self[idxes], idxes, weights + + def update_priorities(self, idxes, priorities): + """Update priorities of sampled transitions. + sets priority of transition at index idxes[i] in buffer + to priorities[i]. + Parameters + ---------- + idxes: [int] + List of idxes of sampled transitions + priorities: [float] + List of updated priorities corresponding to + transitions at the sampled idxes denoted by + variable `idxes`. + """ + assert len(idxes) == len(priorities) + for idx, priority in zip(idxes, priorities): + assert priority > 0 + assert 0 <= idx < self.episodes_in_buffer + self._it_sum[idx] = priority ** self.alpha + self._it_min[idx] = priority ** self.alpha + self.max_priority = max(self.max_priority, priority) diff --git a/src/components/epsilon_schedules.py b/src/components/epsilon_schedules.py new file mode 100644 index 0000000..eb670e6 --- /dev/null +++ b/src/components/epsilon_schedules.py @@ -0,0 +1,40 @@ +import numpy as np + + +class DecayThenFlatSchedule(): + + def __init__(self, + start, + finish, + time_length, + decay="exp"): + + self.start = start + self.finish = finish + self.time_length = time_length + self.delta = (self.start - self.finish) / self.time_length + self.decay = decay + + if self.decay in ["exp"]: + self.exp_scaling = (-1) * self.time_length / np.log(self.finish) if self.finish > 0 else 1 + + def eval(self, T): + if self.decay in ["linear"]: + return max(self.finish, self.start - self.delta * T) + elif self.decay in ["exp"]: + return min(self.start, max(self.finish, np.exp(- T / self.exp_scaling))) + + +class LinearIncreaseSchedule(): + + def __init__(self, + start, + finish, + time_length): + self.start = start + self.finish = finish + self.time_length = time_length + self.delta = (self.start - self.finish) / self.time_length + + def eval(self, T): + return min(self.finish, self.start - self.delta * T) diff --git a/src/components/segment_tree.py b/src/components/segment_tree.py new file mode 100644 index 0000000..263805f --- /dev/null +++ b/src/components/segment_tree.py @@ -0,0 +1,136 @@ +import operator + + +# Directly from OpenAI Baseline implementation (https://github.com/openai/baselines) +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + https://en.wikipedia.org/wiki/Segment_tree + Can be used as regular array, but with two + important differences: + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/src/components/transforms.py b/src/components/transforms.py new file mode 100644 index 0000000..98d72f6 --- /dev/null +++ b/src/components/transforms.py @@ -0,0 +1,22 @@ +import torch as th + + +class Transform: + def transform(self, tensor): + raise NotImplementedError + + def infer_output_info(self, vshape_in, dtype_in): + raise NotImplementedError + + +class OneHot(Transform): + def __init__(self, out_dim): + self.out_dim = out_dim + + def transform(self, tensor): + y_onehot = tensor.new(*tensor.shape[:-1], self.out_dim).zero_() + y_onehot.scatter_(-1, tensor.long(), 1) + return y_onehot.float() + + def infer_output_info(self, vshape_in, dtype_in): + return (self.out_dim,), th.float32 diff --git a/src/config/algs/asn.yaml b/src/config/algs/asn.yaml new file mode 100644 index 0000000..0743d3a --- /dev/null +++ b/src/config/algs/asn.yaml @@ -0,0 +1,35 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "n_mac" +agent: "asn_rnn" +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +#rnn_hidden_dim: 128 # Size of hidden state for default rnn agent +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "asn" diff --git a/src/config/algs/deepset_hyper_qmix.yaml b/src/config/algs/deepset_hyper_qmix.yaml new file mode 100644 index 0000000..e474f4a --- /dev/null +++ b/src/config/algs/deepset_hyper_qmix.yaml @@ -0,0 +1,38 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "deepset_hyper_rnn" +hpn_hyper_dim: 64 +agent_output_type: q + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "deepset_hyper_qmix" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/deepset_hyper_vdn.yaml b/src/config/algs/deepset_hyper_vdn.yaml new file mode 100644 index 0000000..fbc227b --- /dev/null +++ b/src/config/algs/deepset_hyper_vdn.yaml @@ -0,0 +1,38 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "deepset_hyper_rnn" +hpn_hyper_dim: 64 +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "deepset_hyper_vdn" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/deepset_qmix.yaml b/src/config/algs/deepset_qmix.yaml new file mode 100644 index 0000000..e9a2a53 --- /dev/null +++ b/src/config/algs/deepset_qmix.yaml @@ -0,0 +1,38 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "deepset_rnn" +hpn_hyper_dim: 64 +agent_output_type: q + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "deepset_qmix" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/deepset_vdn.yaml b/src/config/algs/deepset_vdn.yaml new file mode 100644 index 0000000..27d5154 --- /dev/null +++ b/src/config/algs/deepset_vdn.yaml @@ -0,0 +1,38 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "deepset_rnn" +hpn_hyper_dim: 64 +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "deepset_vdn" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/feudal.yaml b/src/config/algs/feudal.yaml new file mode 100644 index 0000000..7d7732e --- /dev/null +++ b/src/config/algs/feudal.yaml @@ -0,0 +1,49 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 + +runner: "episode" +batch_size_run: 4 +buffer_size: 5000 +batch_size: 128 + +# update the target network every {} episodes +target_update_interval: 200 + +mac: "feudal_mac" # Basic controller + +# use the Q_Learner to train +agent: "feudal" +# manager +c: 1 # 10 step +manager_hidden_dim: 64 +state_dim: 92 +goal_dim: 16 + +# critic +critic_lr: 0.0005 # +vf_coef: 0.5 # +critic_hidden_dim: 64 # + + +# worker +worker_hidden_dim: 64 +embedding_dim: 16 + + +agent_output_type: "q" +learner: "feudal_learner" +double_q: True +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_layers: 2 +hypernet_embed: 64 +td_lambda: 0.6 + +intrinsic_rewards_alpha: 0.001 + +name: "feudal" diff --git a/src/config/algs/gnn_qmix.yaml b/src/config/algs/gnn_qmix.yaml new file mode 100644 index 0000000..c51e919 --- /dev/null +++ b/src/config/algs/gnn_qmix.yaml @@ -0,0 +1,37 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "gnn_rnn" +gnn_layer_num: 1 +agent_output_type: q + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "gnn_qmix" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/gnn_vdn.yaml b/src/config/algs/gnn_vdn.yaml new file mode 100644 index 0000000..dbdc316 --- /dev/null +++ b/src/config/algs/gnn_vdn.yaml @@ -0,0 +1,37 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +agent: "gnn_rnn" +gnn_layer_num: 1 +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "gnn_vdn" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/hpn_qmix.yaml b/src/config/algs/hpn_qmix.yaml new file mode 100644 index 0000000..b4d0e29 --- /dev/null +++ b/src/config/algs/hpn_qmix.yaml @@ -0,0 +1,43 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 # batch_size_run=4, buffer_size = 2500, batch_size=64 for 3s5z_vs_3s6z +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +#agent: "hpn_rnn" +agent: "hpns_rnn" + +hpn_hyper_dim: 64 +hpn_hyper_activation: 'relu' + +hpn_head_num: 1 # 2 for 3s_vs_5z and 6h_vs_8z +agent_output_type: q + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 # 0.3 for 6h_vs_8z +optimizer: 'adam' +q_lambda: False + + +name: "hpn_qmix" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/hpn_qplex.yaml b/src/config/algs/hpn_qplex.yaml new file mode 100644 index 0000000..327386b --- /dev/null +++ b/src/config/algs/hpn_qplex.yaml @@ -0,0 +1,47 @@ +# From https://github.com/wjh720/QPLEX/ +# --- Qatten specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +# update the target network every {} episodes +target_update_interval: 200 +t_max: 10050000 + +# use the Q_Learner to train +mac: "hpn_mac" +#agent: "hpn_rnn" +agent: "hpns_rnn" +hpn_head_num: 1 + +hpn_hyper_dim: 64 +hpn_hyper_activation: 'relu' + +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +agent_output_type: "q" + +learner: "dmaq_qatten_learner" +double_q: True +mixer: "dmaq" +mixing_embed_dim: 32 +hypernet_embed: 64 +adv_hypernet_layers: 2 +adv_hypernet_embed: 64 +td_lambda: 0.6 +lr: 0.001 + +num_kernel: 4 +is_minus_one: True +weighted_head: True +is_adv_attention: True +is_stop_gradient: True + +name: "hpn_qplex" diff --git a/src/config/algs/hpn_vdn.yaml b/src/config/algs/hpn_vdn.yaml new file mode 100644 index 0000000..457fa1a --- /dev/null +++ b/src/config/algs/hpn_vdn.yaml @@ -0,0 +1,43 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "hpn_mac" +#agent: "hpn_rnn" +agent: "hpns_rnn" + +hpn_hyper_dim: 64 +hpn_hyper_activation: 'relu' +hpn_head_num: 1 # 2 for 3s_vs_5z and 6h_vs_8z +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 + + +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "hpn_vdn" + +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: False # Include the agent's last action (one_hot) in the observation \ No newline at end of file diff --git a/src/config/algs/qmix.yaml b/src/config/algs/qmix.yaml new file mode 100644 index 0000000..8bf5742 --- /dev/null +++ b/src/config/algs/qmix.yaml @@ -0,0 +1,47 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 # batch_size_run=4, buffer_size = 2500, batch_size=64 for 3s5z_vs_3s6z +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "n_mac" +agent: "n_rnn" +agent_output_type: q + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 # 0.3 for 6h_vs_8z +optimizer: 'adam' +q_lambda: False + +# rnn layer normalization +use_layer_norm: False + +# orthogonal init for DNN +use_orthogonal: False +gain: 0.01 + +# Priority experience replay +use_per: False +per_alpha: 0.6 +per_beta: 0.4 +return_priority: False + +#name: "qmix_env=8_adam_td_lambda" +name: "qmix" \ No newline at end of file diff --git a/src/config/algs/qmix_DA.yaml b/src/config/algs/qmix_DA.yaml new file mode 100644 index 0000000..a065f6d --- /dev/null +++ b/src/config/algs/qmix_DA.yaml @@ -0,0 +1,51 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 # batch_size_run=4, buffer_size = 2500, batch_size=64 for 3s5z_vs_3s6z +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "n_mac" +agent: "n_rnn" +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +agent_output_type: q + +learner: "q_learner_data_augmentation" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 # 0.3 for 6h_vs_8z +optimizer: 'adam' +q_lambda: False + +# rnn layer normalization +use_layer_norm: False + +# orthogonal init for DNN +use_orthogonal: False +gain: 0.01 + +# Priority experience replay +use_per: False +per_alpha: 0.6 +per_beta: 0.4 +return_priority: False + +#name: "qmix_env=8_adam_td_lambda" +name: "qmix_DA" + +enable_data_augmentation: True +augment_times: 3 \ No newline at end of file diff --git a/src/config/algs/qplex.yaml b/src/config/algs/qplex.yaml new file mode 100644 index 0000000..6054291 --- /dev/null +++ b/src/config/algs/qplex.yaml @@ -0,0 +1,41 @@ +# From https://github.com/wjh720/QPLEX/ +# --- Qatten specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +# update the target network every {} episodes +target_update_interval: 200 +t_max: 10050000 + +# use the Q_Learner to train +mac: "n_mac" +agent: "n_rnn" +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +agent_output_type: "q" + +learner: "dmaq_qatten_learner" +double_q: True +mixer: "dmaq" +mixing_embed_dim: 32 +hypernet_embed: 64 +adv_hypernet_layers: 2 +adv_hypernet_embed: 64 +td_lambda: 0.6 +lr: 0.001 + +num_kernel: 4 +is_minus_one: True +weighted_head: True +is_adv_attention: True +is_stop_gradient: True + +name: "qplex" diff --git a/src/config/algs/updet_qmix.yaml b/src/config/algs/updet_qmix.yaml new file mode 100644 index 0000000..7b42799 --- /dev/null +++ b/src/config/algs/updet_qmix.yaml @@ -0,0 +1,38 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "updet_mac" +agent: "updet_agent" + +agent_output_type: q +# %%%%%%%%%%%%% Transformer Settings %%%%%%%%%%% +transformer_embed_dim: 32 +transformer_heads: 3 # head number of transformer +transformer_depth: 2 # block number of transformer + +learner: "nq_learner" +mixer: "qmix" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "updet_qmix" \ No newline at end of file diff --git a/src/config/algs/updet_vdn.yaml b/src/config/algs/updet_vdn.yaml new file mode 100644 index 0000000..1e8e14b --- /dev/null +++ b/src/config/algs/updet_vdn.yaml @@ -0,0 +1,38 @@ +# --- QMIX specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "updet_mac" +agent: "updet_agent" + +agent_output_type: q +# %%%%%%%%%%%%% Transformer Settings %%%%%%%%%%% +transformer_embed_dim: 32 +transformer_heads: 3 # head number of transformer +transformer_depth: 2 # block number of transformer + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +name: "updet_vdn" \ No newline at end of file diff --git a/src/config/algs/vdn.yaml b/src/config/algs/vdn.yaml new file mode 100644 index 0000000..e2fed29 --- /dev/null +++ b/src/config/algs/vdn.yaml @@ -0,0 +1,36 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "n_mac" +agent: "n_rnn" +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +#rnn_hidden_dim: 128 # Size of hidden state for default rnn agent +agent_output_type: q + +learner: "nq_learner" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "vdn" diff --git a/src/config/algs/vdn_DA.yaml b/src/config/algs/vdn_DA.yaml new file mode 100644 index 0000000..ad02ddc --- /dev/null +++ b/src/config/algs/vdn_DA.yaml @@ -0,0 +1,38 @@ +# --- VDN specific parameters --- + +# use epsilon greedy action selector +action_selector: "epsilon_greedy" +epsilon_start: 1.0 +epsilon_finish: 0.05 +epsilon_anneal_time: 100000 # 500000 for 6h_vs_8z + +runner: "parallel" +batch_size_run: 8 +buffer_size: 5000 +batch_size: 128 + +t_max: 10050000 + +# update the target network every {} episodes +target_update_interval: 200 + +# use the Q_Learner to train +mac: "n_mac" +agent: "n_rnn" +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +agent_output_type: q + +learner: "q_learner_data_augmentation" +mixer: "vdn" +mixing_embed_dim: 32 +hypernet_embed: 64 +lr: 0.001 # Learning rate for agents +td_lambda: 0.6 +optimizer: 'adam' +q_lambda: False + +#name: "vdn_env=8_adam_td_lambda" +name: "vdn_DA" + +enable_data_augmentation: True +augment_times: 3 diff --git a/src/config/default.yaml b/src/config/default.yaml new file mode 100644 index 0000000..8f22912 --- /dev/null +++ b/src/config/default.yaml @@ -0,0 +1,57 @@ +# --- Defaults --- + +# --- pymarl options --- +runner: "episode" # Runs 1 env for an episode +mac: "basic_mac" # Basic controller +env: "sc2" # Environment name +env_args: {} # Arguments for the environment +batch_size_run: 1 # Number of environments to run in parallel +test_nepisode: 20 # Number of episodes to test for +test_interval: 2000 # Test after {} timesteps have passed +test_greedy: True # Use greedy evaluation (if False, will set epsilon floor to 0 +log_interval: 2000 # Log summary of stats after every {} timesteps +runner_log_interval: 2000 # Log runner stats (not test stats) every {} timesteps +learner_log_interval: 2000 # Log training stats every {} timesteps +t_max: 10000 # Stop running after this many timesteps +use_cuda: True # Use gpu by default unless it isn't available +buffer_cpu_only: True # If true we won't keep all of the replay buffer in vram + +# --- Logging options --- +use_tensorboard: True # Log results to tensorboard +save_model: True # Save the models to disk +save_model_interval: 2000000 # Save models after this many timesteps +checkpoint_path: "" # Load a checkpoint from this path +evaluate: False # Evaluate model for test_nepisode episodes and quit (no training) +load_step: 0 # Load model trained on this many timesteps (0 if choose max possible) +save_replay: False # Saving the replay of the model loaded from checkpoint_path +local_results_path: "results" # Path for local results + +# --- RL hyperparameters --- +gamma: 0.99 +batch_size: 32 # Number of episodes to train on +buffer_size: 32 # Size of the replay buffer +lr: 0.0005 # Learning rate for agents +critic_lr: 0.0005 # Learning rate for critics +optim_alpha: 0.99 # RMSProp alpha +optim_eps: 0.00001 # RMSProp epsilon +grad_norm_clip: 10 # Reduce magnitude of gradients above this L2 norm +double_q: True + +# --- Agent parameters --- +agent: "rnn" # Default rnn agent +rnn_hidden_dim: 64 # Size of hidden state for default rnn agent +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: True # Include the agent's last action (one_hot) in the observation + +# --- Experiment running params --- +repeat_id: 1 +label: "default_label" + +run: "default" + +thread_num: 4 # 12 +enable_parallel_computing: False + +cpu_inference: True +#cpu_inference: False +asn_hidden_size: 32 # 64 diff --git a/src/config/envs/sc2.yaml b/src/config/envs/sc2.yaml new file mode 100644 index 0000000..47c754a --- /dev/null +++ b/src/config/envs/sc2.yaml @@ -0,0 +1,42 @@ +env: sc2 + +env_args: + continuing_episode: False + difficulty: "7" + game_version: null + map_name: "3m" + move_amount: 2 + obs_all_health: True + obs_instead_of_state: False + obs_last_action: False + obs_own_health: True + obs_pathing_grid: False + obs_terrain_height: False + obs_timestep_number: False + reward_death_value: 10 + reward_defeat: 0 + reward_negative_scale: 0.5 + reward_only_positive: True + reward_scale: True + reward_scale_rate: 20 + reward_sparse: False + reward_win: 200 + replay_dir: "" + replay_prefix: "" + state_last_action: True + state_timestep_number: False + step_mul: 8 + seed: null + heuristic_ai: False + heuristic_rest: False + debug: False + +test_greedy: True +test_nepisode: 32 +test_interval: 10000 +log_interval: 10000 +runner_log_interval: 10000 +learner_log_interval: 10000 +t_max: 2050000 +obs_agent_id: True # Include the agent's one_hot id in the observation +obs_last_action: True # Include the agent's last action (one_hot) in the observation diff --git a/src/config/envs/sc2_v2_protoss.yaml b/src/config/envs/sc2_v2_protoss.yaml new file mode 100644 index 0000000..0480bf5 --- /dev/null +++ b/src/config/envs/sc2_v2_protoss.yaml @@ -0,0 +1,76 @@ +env: sc2_v2 + +env_args: + change_fov_with_move: False # if True, we split the full field-of-view into 4 90-degree sectors (instead of 12 30-degree sectors), each of which corresponds to `move north, south, east, west'. + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + capability_config: + n_units: 5 + team_gen: + dist_type: "weighted_teams" + unit_types: + - "stalker" + - "zealot" + - "colossus" + weights: + - 0.45 + - 0.45 + - 0.1 + exception_unit_types: + - "colossus" + observe: True + + start_positions: + dist_type: "surrounded_and_reflect" + p: 0.5 + n_enemies: 5 + map_x: 32 + map_y: 32 + + map_name: "10gen_protoss" + obs_own_pos: True + obs_starcraft: True + # conic_fov: True + # Since our target is not to design more efficient exploration algorithms, we keep the field-of-view and attack of the agents a full circle as in SMAC-V1. + conic_fov: False + num_fov_actions: 12 + kill_unit_step_mul: 2 + fully_observable: False + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + + + continuing_episode: False + difficulty: "7" + game_version: null + move_amount: 2 + obs_all_health: True + obs_instead_of_state: False + obs_last_action: False + obs_own_health: True + obs_pathing_grid: False + obs_terrain_height: False + obs_timestep_number: False + reward_death_value: 10 + reward_defeat: 0 + reward_negative_scale: 0.5 + reward_only_positive: True + reward_scale: True + reward_scale_rate: 20 + reward_sparse: False + reward_win: 200 + replay_dir: "" + replay_prefix: "" + state_last_action: True + state_timestep_number: False + step_mul: 8 + seed: null + heuristic_ai: False + heuristic_rest: False + debug: False + +test_greedy: True +test_nepisode: 32 +test_interval: 10000 +log_interval: 10000 +runner_log_interval: 10000 +learner_log_interval: 10000 +t_max: 2050000 diff --git a/src/config/envs/sc2_v2_terran.yaml b/src/config/envs/sc2_v2_terran.yaml new file mode 100644 index 0000000..4374289 --- /dev/null +++ b/src/config/envs/sc2_v2_terran.yaml @@ -0,0 +1,76 @@ +env: sc2_v2 + +env_args: + change_fov_with_move: False # if True, we split the full field-of-view into 4 90-degree sectors (instead of 12 30-degree sectors), each of which corresponds to `move north, south, east, west'. + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + capability_config: + n_units: 5 + team_gen: + dist_type: "weighted_teams" + unit_types: + - "marine" + - "marauder" + - "medivac" + weights: + - 0.45 + - 0.45 + - 0.1 + exception_unit_types: + - "medivac" + observe: True + + start_positions: + dist_type: "surrounded_and_reflect" + p: 0.5 + n_enemies: 5 + map_x: 32 + map_y: 32 + + map_name: "10gen_terran" + obs_own_pos: True + obs_starcraft: True + # conic_fov: True + # Since our target is not to design more efficient exploration algorithms, we keep the field-of-view and attack of the agents a full circle as in SMAC-V1. + conic_fov: False + num_fov_actions: 12 + kill_unit_step_mul: 2 + fully_observable: False + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + + + continuing_episode: False + difficulty: "7" + game_version: null + move_amount: 2 + obs_all_health: True + obs_instead_of_state: False + obs_last_action: False + obs_own_health: True + obs_pathing_grid: False + obs_terrain_height: False + obs_timestep_number: False + reward_death_value: 10 + reward_defeat: 0 + reward_negative_scale: 0.5 + reward_only_positive: True + reward_scale: True + reward_scale_rate: 20 + reward_sparse: False + reward_win: 200 + replay_dir: "" + replay_prefix: "" + state_last_action: True + state_timestep_number: False + step_mul: 8 + seed: null + heuristic_ai: False + heuristic_rest: False + debug: False + +test_greedy: True +test_nepisode: 32 +test_interval: 10000 +log_interval: 10000 +runner_log_interval: 10000 +learner_log_interval: 10000 +t_max: 2050000 diff --git a/src/config/envs/sc2_v2_zerg.yaml b/src/config/envs/sc2_v2_zerg.yaml new file mode 100644 index 0000000..1760958 --- /dev/null +++ b/src/config/envs/sc2_v2_zerg.yaml @@ -0,0 +1,76 @@ +env: sc2_v2 + +env_args: + change_fov_with_move: False # if True, we split the full field-of-view into 4 90-degree sectors (instead of 12 30-degree sectors), each of which corresponds to `move north, south, east, west'. + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + capability_config: + n_units: 5 + team_gen: + dist_type: "weighted_teams" + unit_types: + - "zergling" + - "hydralisk" + - "baneling" + weights: + - 0.45 + - 0.45 + - 0.1 + exception_unit_types: + - "baneling" + observe: True + + start_positions: + dist_type: "surrounded_and_reflect" + p: 0.5 + n_enemies: 5 + map_x: 32 + map_y: 32 + + map_name: "10gen_zerg" + obs_own_pos: True + obs_starcraft: True + # conic_fov: True + # Since our target is not to design more efficient exploration algorithms, we keep the field-of-view and attack of the agents a full circle as in SMAC-V1. + conic_fov: False + num_fov_actions: 12 + kill_unit_step_mul: 2 + fully_observable: False + # %%%%%%%%%%%%%%%%%%%%%% new config compared to v1 %%%%%%%%%%%%%%%%%%%%%% + + + continuing_episode: False + difficulty: "7" + game_version: null + move_amount: 2 + obs_all_health: True + obs_instead_of_state: False + obs_last_action: False + obs_own_health: True + obs_pathing_grid: False + obs_terrain_height: False + obs_timestep_number: False + reward_death_value: 10 + reward_defeat: 0 + reward_negative_scale: 0.5 + reward_only_positive: True + reward_scale: True + reward_scale_rate: 20 + reward_sparse: False + reward_win: 200 + replay_dir: "" + replay_prefix: "" + state_last_action: True + state_timestep_number: False + step_mul: 8 + seed: null + heuristic_ai: False + heuristic_rest: False + debug: False + +test_greedy: True +test_nepisode: 32 +test_interval: 10000 +log_interval: 10000 +runner_log_interval: 10000 +learner_log_interval: 10000 +t_max: 2050000 diff --git a/src/controllers/FeUdal_controller.py b/src/controllers/FeUdal_controller.py new file mode 100644 index 0000000..0b2f4e7 --- /dev/null +++ b/src/controllers/FeUdal_controller.py @@ -0,0 +1,164 @@ +#from modules.agents import REGISTRY as agent_REGISTRY +from modules.agents.FeUdal_agent import Feudal_ManagerAgent, Feudal_WorkerAgent +from components.action_selectors import REGISTRY as action_REGISTRY +import torch + + +# This multi-agent controller shares parameters between agents +class FeUdalMAC: + def __init__(self, scheme, groups, args): + self.n_agents = args.n_agents + self.args = args + self.input_shape = self._get_input_shape(scheme) + self.manager_input_shape = self._get_manager_input_shape(scheme) + self._build_agents(self.manager_input_shape, self.input_shape) + self.agent_output_type = args.agent_output_type + + self.action_selector = action_REGISTRY[args.action_selector](args) + + self.manager_hidden_states = None + self.manager_cell_states = None + self.worker_hidden_states = None + self.worker_cell_states = None + + self.single_past_goals = [] + self.batch_past_goals = [] + + def select_actions(self, ep_batch, t_ep, t_env, bs=slice(None), test_mode=False): + # Only select actions for the selected batch elements in bs + avail_actions = ep_batch["avail_actions"][:, t_ep] + agent_outputs, goal_outs, value_outs = self.forward(ep_batch, t_ep, test_mode=test_mode) + chosen_actions = self.action_selector.select_action(agent_outputs[bs], avail_actions[bs], t_env, test_mode=test_mode) + return chosen_actions + + def forward(self, ep_batch, t, test_mode=False): + manager_inputs = self._build_manager_inputs(ep_batch, t) + worker_inputs = self._build_inputs(ep_batch, t) + + # 更新為處理 LSTM 的 hidden states 和 cell states + manager_goal, manager_value, (new_manager_hidden, new_manager_cell) = self.manager_agent( + manager_inputs, + (self.manager_hidden_states, self.manager_cell_states) + ) + self.manager_hidden_states = new_manager_hidden + self.manager_cell_states = new_manager_cell + + worker_agent_outs, (new_worker_hidden, new_worker_cell), self.single_past_goals, self.batch_past_goals = self.worker_agent( + worker_inputs, + (self.worker_hidden_states, self.worker_cell_states), + self.single_past_goals, + self.batch_past_goals, + manager_goal + ) + self.worker_hidden_states = new_worker_hidden + self.worker_cell_states = new_worker_cell + + return worker_agent_outs.view(ep_batch.batch_size, self.n_agents, -1), manager_goal.view(ep_batch.batch_size, self.n_agents, -1), manager_value.view(ep_batch.batch_size, self.n_agents, -1) + + def init_hidden(self, batch_size): + # 初始化 LSTM 的 hidden states 和 cell states + manager_hidden, manager_cell = self.manager_agent.init_hidden() + worker_hidden, worker_cell = self.worker_agent.init_hidden() + + self.manager_hidden_states = manager_hidden.unsqueeze(0).expand(batch_size, self.n_agents, -1) + self.manager_cell_states = manager_cell.unsqueeze(0).expand(batch_size, self.n_agents, -1) + self.worker_hidden_states = worker_hidden.unsqueeze(0).expand(batch_size, self.n_agents, -1) + self.worker_cell_states = worker_cell.unsqueeze(0).expand(batch_size, self.n_agents, -1) + + def parameters(self): + """返回所有可訓練參數""" + # 將生成器轉換為列表後再合併 + manager_params = list(self.manager_agent.parameters()) + worker_params = list(self.worker_agent.parameters()) + return manager_params + worker_params + + def manager_parameters(self): + return self.manager_agent.parameters() + + def worker_parameters(self): + return self.worker_agent.parameters() + + def load_state(self, other_mac): + self.manager_agent.load_state_dict(other_mac.manager_agent.state_dict()) + self.worker_agent.load_state_dict(other_mac.worker_agent.state_dict()) + + def cuda(self): + self.manager_agent.cuda() + self.worker_agent.cuda() + + def cpu(self): + self.manager_agent.cpu() + self.worker_agent.cpu() + + def get_device(self): + """返回模型所在的設備（CPU或GPU）""" + # 直接檢查第一個參數的設備 + if hasattr(self, 'manager_agent'): + return next(self.manager_agent.parameters()).device + elif hasattr(self, 'worker_agent'): + return next(self.worker_agent.parameters()).device + return torch.device("cpu") # 默認返回 CPU + + def save_models(self, path): + torch.save(self.manager_agent.state_dict(), "{}/manager_agent.torch".format(path)) + torch.save(self.worker_agent.state_dict(), "{}/worker_agent.torch".format(path)) + + + def load_models(self, path): + self.manager_agent.load_state_dict(torch.load("{}/manager_agent.torch".format(path), map_location=lambda storage, loc: storage)) + self.worker_agent.load_state_dict(torch.load("{}/worker_agent.torch".format(path), map_location=lambda storage, loc: storage)) + + def _build_agents(self, manager_input_shape, worker_input_shape): + self.manager_agent = Feudal_ManagerAgent(manager_input_shape, self.args) + self.worker_agent = Feudal_WorkerAgent(worker_input_shape, self.args) + + def _build_inputs(self, batch, t): + # Assumes homogenous agents with flat observations. + # Other MACs might want to e.g. delegate building inputs to each agent + bs = batch.batch_size + inputs = [] + inputs.append(batch["obs"][:, t]) # b1av + if self.args.obs_last_action: + if t == 0: + inputs.append(torch.zeros_like(batch["actions_onehot"][:, t])) + else: + inputs.append(batch["actions_onehot"][:, t-1]) + if self.args.obs_agent_id: + inputs.append(torch.eye(self.n_agents, device=batch.device).unsqueeze(0).expand(bs, -1, -1)) + + inputs = torch.cat([x.reshape(bs*self.n_agents, -1) for x in inputs], dim=1) + return inputs + + def _get_input_shape(self, scheme): + input_shape = scheme["obs"]["vshape"] + if self.args.obs_last_action: + input_shape += scheme["actions_onehot"]["vshape"][0] + if self.args.obs_agent_id: + input_shape += self.n_agents + + return input_shape + + def _build_manager_inputs(self, batch, t): + # 假設 manager 需要不同的輸入結構 + bs = batch.batch_size + manager_inputs = [] + manager_inputs.append(batch["obs"][:, t]) # 假設有一個 manager 特定的觀察 + # if self.args.obs_last_action: + # if t == 0: + # manager_inputs.append(torch.zeros_like(batch["actions_onehot"][:, t])) + # else: + # manager_inputs.append(batch["actions_onehot"][:, t-1]) + # # 不需要代理ID，移除以下程式碼 + # if self.args.obs_agent_id: + # manager_inputs.append(torch.eye(self.n_agents, device=batch.device).unsqueeze(0).expand(bs, -1, -1)) + + manager_inputs = torch.cat([x.reshape(bs*self.n_agents, -1) for x in manager_inputs], dim=1) + return manager_inputs + + def _get_manager_input_shape(self, scheme): + input_shape = scheme["obs"]["vshape"] + # if self.args.manager_obs_last_action: + # input_shape += scheme["manager_actions_onehot"]["vshape"][0] + # if self.args.manager_obs_agent_id: + # input_shape += self.n_agents + return input_shape diff --git a/src/controllers/__init__.py b/src/controllers/__init__.py new file mode 100644 index 0000000..d4a323a --- /dev/null +++ b/src/controllers/__init__.py @@ -0,0 +1,12 @@ +REGISTRY = {} + +from .hpn_controller import HPNMAC +from .basic_controller import BasicMAC +from .n_controller import NMAC +from .updet_controller import UPDETController +from .FeUdal_controller import FeUdalMAC +REGISTRY["basic_mac"] = BasicMAC +REGISTRY["n_mac"] = NMAC +REGISTRY["hpn_mac"] = HPNMAC +REGISTRY["updet_mac"] = UPDETController +REGISTRY["feudal_mac"] = FeUdalMAC diff --git a/src/controllers/basic_controller.py b/src/controllers/basic_controller.py new file mode 100644 index 0000000..f042491 --- /dev/null +++ b/src/controllers/basic_controller.py @@ -0,0 +1,111 @@ +import os + +from modules.agents import REGISTRY as agent_REGISTRY +from components.action_selectors import REGISTRY as action_REGISTRY +import torch as th +from utils.th_utils import get_parameters_num + + +# This multi-agent controller shares parameters between agents +class BasicMAC: + def __init__(self, scheme, groups, args): + self.n_agents = args.n_agents + self.args = args + self.input_shape = self._get_input_shape(scheme) + self._build_agents(self.input_shape) + self.agent_output_type = args.agent_output_type + + self.action_selector = action_REGISTRY[args.action_selector](args) + self.save_probs = getattr(self.args, 'save_probs', False) + + self.hidden_states = None + + def select_actions(self, ep_batch, t_ep, t_env, bs=slice(None), test_mode=False): + if t_ep == 0: + self.set_evaluation_mode() + # Only select actions for the selected batch elements in bs + avail_actions = ep_batch["avail_actions"][:, t_ep] + agent_outputs = self.forward(ep_batch, t_ep, test_mode=test_mode) + chosen_actions = self.action_selector.select_action(agent_outputs[bs], avail_actions[bs], t_env, test_mode=test_mode) + return chosen_actions + + def forward(self, ep_batch, t, test_mode=False): + agent_inputs = self._build_inputs(ep_batch, t) + avail_actions = ep_batch["avail_actions"][:, t] + agent_outs, self.hidden_states = self.agent(agent_inputs, self.hidden_states) + + # Softmax the agent outputs if they're policy logits + if self.agent_output_type == "pi_logits": + if getattr(self.args, "mask_before_softmax", True): + # Make the logits for unavailable actions very negative to minimise their affect on the softmax + agent_outs = agent_outs.reshape(ep_batch.batch_size * self.n_agents, -1) + reshaped_avail_actions = avail_actions.reshape(ep_batch.batch_size * self.n_agents, -1) + agent_outs[reshaped_avail_actions == 0] = -1e10 + + agent_outs = th.nn.functional.softmax(agent_outs, dim=-1) + + return agent_outs.view(ep_batch.batch_size, self.n_agents, -1) + + def init_hidden(self, batch_size): + self.hidden_states = self.agent.init_hidden() + if self.hidden_states is not None: + self.hidden_states = self.hidden_states.unsqueeze(0).expand(batch_size, self.n_agents, -1) # bav + + def set_train_mode(self): + self.agent.train() + + def set_evaluation_mode(self): + self.agent.eval() + + def parameters(self): + return self.agent.parameters() + + def load_state(self, other_mac): + self.agent.load_state_dict(other_mac.agent.state_dict()) + + def cuda(self): + self.agent.cuda() + + def cpu(self): + self.agent.cpu() + + def get_device(self): + return next(self.parameters()).device + + def save_models(self, path): + th.save(self.agent.state_dict(), "{}/agent.th".format(path)) + + def load_models(self, path): + self.agent.load_state_dict(th.load("{}/agent.th".format(path), map_location=lambda storage, loc: storage)) + + def _build_agents(self, input_shape): + self.agent = agent_REGISTRY[self.args.agent](input_shape, self.args) + #print("&&&&&&&&&&&&&&&&&&&&&&", self.args.agent, get_parameters_num(self.parameters())) + # for p in list(self.parameters()): + # print(p.shape) + + def _build_inputs(self, batch, t): + # Assumes homogenous agents with flat observations. + # Other MACs might want to e.g. delegate building inputs to each agent + bs = batch.batch_size + inputs = [] + inputs.append(batch["obs"][:, t]) # b1av + if self.args.obs_last_action: + if t == 0: + inputs.append(th.zeros_like(batch["actions_onehot"][:, t])) + else: + inputs.append(batch["actions_onehot"][:, t-1]) + if self.args.obs_agent_id: + inputs.append(th.eye(self.n_agents, device=batch.device).unsqueeze(0).expand(bs, -1, -1)) + + inputs = th.cat([x.reshape(bs, self.n_agents, -1) for x in inputs], dim=-1) + return inputs + + def _get_input_shape(self, scheme): + input_shape = scheme["obs"]["vshape"] + if self.args.obs_last_action: + input_shape += scheme["actions_onehot"]["vshape"][0] + if self.args.obs_agent_id: + input_shape += self.n_agents + + return input_shape diff --git a/src/controllers/hpn_controller.py b/src/controllers/hpn_controller.py new file mode 100644 index 0000000..8824f6b --- /dev/null +++ b/src/controllers/hpn_controller.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +import numpy as np +import torch as th + +from .basic_controller import BasicMAC + + +class DataParallelAgent(th.nn.DataParallel): + def init_hidden(self): + # make hidden states on same device as model + return self.module.init_hidden() + + +# This multi-agent controller shares parameters between agents +class HPNMAC(BasicMAC): + def __init__(self, scheme, groups, args): + super(HPNMAC, self).__init__(scheme, groups, args) + self.n_enemies = args.n_enemies + self.n_allies = self.n_agents - 1 + + # Add new func + def _get_obs_component_dim(self): + move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim = self.args.obs_component # [4, (6, 5), (4, 5), 1] + enemy_feats_dim_flatten = np.prod(enemy_feats_dim) + ally_feats_dim_flatten = np.prod(ally_feats_dim) + return (move_feats_dim, enemy_feats_dim_flatten, ally_feats_dim_flatten, own_feats_dim), ( + enemy_feats_dim, ally_feats_dim) + + def _build_inputs(self, batch, t): + bs = batch.batch_size + obs_component_dim, _ = self._get_obs_component_dim() + raw_obs_t = batch["obs"][:, t] # [batch, agent_num, obs_dim] + move_feats_t, enemy_feats_t, ally_feats_t, own_feats_t = th.split(raw_obs_t, obs_component_dim, dim=-1) + enemy_feats_t = enemy_feats_t.reshape(bs * self.n_agents * self.n_enemies, + -1) # [bs * n_agents * n_enemies, fea_dim] + ally_feats_t = ally_feats_t.reshape(bs * self.n_agents * self.n_allies, + -1) # [bs * n_agents * n_allies, a_fea_dim] + # merge move features and own features to simplify computation. + context_feats = [move_feats_t, own_feats_t] # [batch, agent_num, own_dim] + own_context = th.cat(context_feats, dim=2).reshape(bs * self.n_agents, -1) # [bs * n_agents, own_dim] + + embedding_indices = [] + if self.args.obs_agent_id: + # agent-id indices, [bs, n_agents] + embedding_indices.append(th.arange(self.n_agents, device=batch.device).unsqueeze(0).expand(bs, -1)) + if self.args.obs_last_action: + # action-id indices, [bs, n_agents] + if t == 0: + embedding_indices.append(None) + else: + embedding_indices.append(batch["actions"][:, t - 1].squeeze(-1)) + + return bs, own_context, enemy_feats_t, ally_feats_t, embedding_indices + + def _get_input_shape(self, scheme): + move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim = self.args.obs_component + own_context_dim = move_feats_dim + own_feats_dim + return own_context_dim, enemy_feats_dim, ally_feats_dim diff --git a/src/controllers/n_controller.py b/src/controllers/n_controller.py new file mode 100644 index 0000000..9a6a6eb --- /dev/null +++ b/src/controllers/n_controller.py @@ -0,0 +1,28 @@ +import os + +from modules.agents import REGISTRY as agent_REGISTRY +from components.action_selectors import REGISTRY as action_REGISTRY +from .basic_controller import BasicMAC +import torch as th +from utils.rl_utils import RunningMeanStd +import numpy as np + +# This multi-agent controller shares parameters between agents +class NMAC(BasicMAC): + def __init__(self, scheme, groups, args): + super(NMAC, self).__init__(scheme, groups, args) + + def select_actions(self, ep_batch, t_ep, t_env, bs=slice(None), test_mode=False): + if t_ep == 0: + self.set_evaluation_mode() + # Only select actions for the selected batch elements in bs + avail_actions = ep_batch["avail_actions"][:, t_ep] + qvals = self.forward(ep_batch, t_ep, test_mode=test_mode) + chosen_actions = self.action_selector.select_action(qvals[bs], avail_actions[bs], t_env, test_mode=test_mode) + return chosen_actions + + def forward(self, ep_batch, t, test_mode=False): + agent_inputs = self._build_inputs(ep_batch, t) + # avail_actions = ep_batch["avail_actions"][:, t] + agent_outs, self.hidden_states = self.agent(agent_inputs, self.hidden_states) + return agent_outs \ No newline at end of file diff --git a/src/controllers/updet_controller.py b/src/controllers/updet_controller.py new file mode 100644 index 0000000..50ab6ee --- /dev/null +++ b/src/controllers/updet_controller.py @@ -0,0 +1,73 @@ +from .basic_controller import BasicMAC +import torch as th +import numpy as np +import torch.nn.functional as F + + +class UPDETController(BasicMAC): + def select_actions(self, ep_batch, t_ep, t_env, bs=slice(None), test_mode=False): + if t_ep == 0: + self.set_evaluation_mode() + # Only select actions for the selected batch elements in bs + avail_actions = ep_batch["avail_actions"][:, t_ep] + agent_outputs = self.forward(ep_batch, t_ep, test_mode=test_mode) + chosen_actions = self.action_selector.select_action(agent_outputs[bs], avail_actions[bs], t_env, + test_mode=test_mode) + return chosen_actions + + def _get_obs_shape(self): + size = 0 + for comp in self.args.obs_component: + if isinstance(comp, int): + size += comp + else: + size += np.prod(comp) + return size + + def _get_obs_component_dim(self): + move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim = self.args.obs_component # [4, (6, 5), (4, 5), 1] + enemy_feats_dim = np.prod(enemy_feats_dim) + ally_feats_dim = np.prod(ally_feats_dim) + return move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim + + def _build_inputs(self, batch, t): + bs = batch.batch_size + raw_obs = batch["obs"][:, t] # [batch, agent_num, obs_dim] + # assert raw_obs.shape[-1] == self._get_obs_shape() + obs_component_dim = self._get_obs_component_dim() + move_feats, enemy_feats, ally_feats, own_feats = th.split(raw_obs, obs_component_dim, dim=-1) + own_context = th.cat((own_feats, move_feats), dim=2) + # use the max_dim (over self, enemy and ally) to init the token layer (to support all maps) + token_dim = max([self.input_shape[0], self.input_shape[1][-1], self.input_shape[2][-1]]) + + own_context = own_context.contiguous().view(bs * self.n_agents, 1, -1) + enemy_feats = enemy_feats.contiguous().view(bs * self.n_agents, self.args.n_enemies, -1) + ally_feats = ally_feats.contiguous().view(bs * self.n_agents, (self.args.n_agents - 1), -1) + + # In the original repository, UPDeT only supports marine-based battle scenarios. e.g. 3m, 8m, 5m_vs_6m, whose feature_dim is the same + # We do zero paddings here to support all maps + inputs = th.cat([ + self.zero_padding(own_context, token_dim), + self.zero_padding(enemy_feats, token_dim), + self.zero_padding(ally_feats, token_dim) + ], dim=1) + + return inputs + + def _get_input_shape(self, scheme): + move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim = self.args.obs_component + own_context_dim = move_feats_dim + own_feats_dim + return own_context_dim, enemy_feats_dim, ally_feats_dim + + def zero_padding(self, features, token_dim): + """ + :param features: [bs * n_agents, k, fea_dim] + :param token_dim: maximum of fea_dim + :return: + """ + existing_dim = features.shape[-1] + if existing_dim < token_dim: + # padding to the right side of the last dimension of the feature. + return F.pad(features, pad=[0, token_dim - existing_dim], mode='constant', value=0) + else: + return features diff --git a/src/envs/__init__.py b/src/envs/__init__.py new file mode 100644 index 0000000..a92cb2d --- /dev/null +++ b/src/envs/__init__.py @@ -0,0 +1,44 @@ +from functools import partial +import sys +import os + +from .multiagentenv import MultiAgentEnv + +try: + smac = True + from .smac_v1 import StarCraft2EnvWrapper +except Exception as e: + print(e) + smac = False + +try: + smacv2 = True + from .smac_v2 import StarCraft2Env2Wrapper +except Exception as e: + print(e) + smacv2 = False + + +def env_fn(env, **kwargs) -> MultiAgentEnv: + return env(**kwargs) + + +REGISTRY = {} + +if smac: + REGISTRY["sc2"] = partial(env_fn, env=StarCraft2EnvWrapper) + if sys.platform == "linux": + os.environ.setdefault("SC2PATH", + os.path.join(os.getcwd(), "3rdparty", "StarCraftII")) +else: + print("SMAC V1 is not supported...") + +if smacv2: + REGISTRY["sc2_v2"] = partial(env_fn, env=StarCraft2Env2Wrapper) + if sys.platform == "linux": + os.environ.setdefault("SC2PATH", + os.path.join(os.getcwd(), "3rdparty", "StarCraftII")) +else: + print("SMAC V2 is not supported...") + +print("Supported environments:", REGISTRY) diff --git a/src/envs/multiagentenv.py b/src/envs/multiagentenv.py new file mode 100644 index 0000000..9c311f3 --- /dev/null +++ b/src/envs/multiagentenv.py @@ -0,0 +1,60 @@ +class MultiAgentEnv(object): + + def step(self, actions): + """ Returns reward, terminated, info """ + raise NotImplementedError + + def get_obs(self): + """ Returns all agent observations in a list """ + raise NotImplementedError + + def get_obs_agent(self, agent_id): + """ Returns observation for agent_id """ + raise NotImplementedError + + def get_obs_size(self): + """ Returns the shape of the observation """ + raise NotImplementedError + + def get_state(self): + raise NotImplementedError + + def get_state_size(self): + """ Returns the shape of the state""" + raise NotImplementedError + + def get_avail_actions(self): + raise NotImplementedError + + def get_avail_agent_actions(self, agent_id): + """ Returns the available actions for agent_id """ + raise NotImplementedError + + def get_total_actions(self): + """ Returns the total number of actions an agent could ever take """ + # TODO: This is only suitable for a discrete 1 dimensional action space for each agent + raise NotImplementedError + + def reset(self): + """ Returns initial observations and states""" + raise NotImplementedError + + def render(self): + raise NotImplementedError + + def close(self): + raise NotImplementedError + + def seed(self): + raise NotImplementedError + + def save_replay(self): + raise NotImplementedError + + def get_env_info(self): + env_info = {"state_shape": self.get_state_size(), + "obs_shape": self.get_obs_size(), + "n_actions": self.get_total_actions(), + "n_agents": self.n_agents, + "episode_limit": self.episode_limit} + return env_info diff --git a/src/envs/smac_v1/StarCraft2EnvWrapper.py b/src/envs/smac_v1/StarCraft2EnvWrapper.py new file mode 100644 index 0000000..8ebc8bc --- /dev/null +++ b/src/envs/smac_v1/StarCraft2EnvWrapper.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +Project: API-Network +File: StarCraft2EnvWrapper.py +Author: Hao Xiaotian +Date: 2022/6/13 16:26 +""" + +from .official.starcraft2 import StarCraft2Env + +class StarCraft2EnvWrapper(StarCraft2Env): + + # Add new functions to support permutation operation + def get_obs_component(self): + move_feats_dim = self.get_obs_move_feats_size() + enemy_feats_dim = self.get_obs_enemy_feats_size() + ally_feats_dim = self.get_obs_ally_feats_size() + own_feats_dim = self.get_obs_own_feats_size() + obs_component = [move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim] + return obs_component + + def get_state_component(self): + if self.obs_instead_of_state: + return [self.get_obs_size()] * self.n_agents + + nf_al = 4 + self.shield_bits_ally + self.unit_type_bits + nf_en = 3 + self.shield_bits_enemy + self.unit_type_bits + + enemy_state = self.n_enemies * nf_en + ally_state = self.n_agents * nf_al + + size = [ally_state, enemy_state] + + if self.state_last_action: + size.append(self.n_agents * self.n_actions) + if self.state_timestep_number: + size.append(1) + return size + + def get_env_info(self): + print("Starting to get environment info...") # 改用英文 + env_info = { + "state_shape": self.get_state_size(), + "obs_shape": self.get_obs_size(), + "n_actions": self.get_total_actions(), + "n_agents": self.n_agents, + "n_enemies": self.n_enemies, + "episode_limit": self.episode_limit, + + "n_normal_actions": self.n_actions_no_attack, + "n_allies": self.n_agents - 1, + # "obs_ally_feats_size": self.get_obs_ally_feats_size(), + # "obs_enemy_feats_size": self.get_obs_enemy_feats_size(), + "state_ally_feats_size": self.get_ally_num_attributes(), # 4 + self.shield_bits_ally + self.unit_type_bits, + "state_enemy_feats_size": self.get_enemy_num_attributes(), + # 3 + self.shield_bits_enemy + self.unit_type_bits, + "obs_component": self.get_obs_component(), + "state_component": self.get_state_component(), + "map_type": self.map_type, + } + print("Environment info:", env_info) # 改用英文 + return env_info + + def _get_medivac_ids(self): + print("Starting to get medivac IDs...") # 改用英文 + medivac_ids = [] + for al_id, al_unit in self.agents.items(): + if self.map_type == "MMM" and al_unit.unit_type == self.medivac_id: + medivac_ids.append(al_id) + print("Medivac IDs:", medivac_ids) # 改用英文 + return medivac_ids + + # def reward_battle(self): + # """Reward function when self.reward_spare==False. + # + # Fix the **REWARD FUNCTION BUG** of the original starcraft2.py. + # + # We carefully check the code and indeed find some code error in starcraft2.py. + # The error is caused by the incorrect reward calculation for the shield regeneration process and this error will + # only occur for scenarios where the enemies are Protoss units. + # + # (1) At line 717 of reward_battle() of starcraft2.py, the reward is computed as: reward = abs(delta_enemy). + # Normally, when the agents attack the enemies, delta_enemy will > 0 and thus the agents will be rewarded for attacking enemies. + # + # (2) For Protoss enemies, delta_enemy can < 0 due to the shield regeneration. However, due to the abs() taken over delta_enemy, + # the agents will still be rewarded when the enemies' shields regenerate. This incorrect reward will lead to undesired behaviors, + # e.g., attacking the enemies but not killing them and waiting their shields regenerating. + # + # (3) Due to the PI/PE design and the improved representational capacity, HPN-QMIX is more sensitive to such + # incorrect rewards and sometimes learn strange behaviors. + # + # Returns accumulative hit/shield point damage dealt to the enemy + # + reward_death_value per enemy unit killed, and, in case + # self.reward_only_positive == False, - (damage dealt to ally units + # + reward_death_value per ally unit killed) * self.reward_negative_scale + # """ + # if self.reward_sparse: + # return 0 + # + # reward = 0 + # delta_deaths = 0 + # delta_ally = 0 + # delta_enemy = 0 + # + # neg_scale = self.reward_negative_scale + # + # # update deaths + # for al_id, al_unit in self.agents.items(): + # if not self.death_tracker_ally[al_id]: + # # did not die so far + # prev_health = ( + # self.previous_ally_units[al_id].health + # + self.previous_ally_units[al_id].shield + # ) + # if al_unit.health == 0: + # # just died + # self.death_tracker_ally[al_id] = 1 + # if not self.reward_only_positive: + # delta_deaths -= self.reward_death_value * neg_scale + # delta_ally += prev_health * neg_scale + # else: + # # still alive + # delta_ally += neg_scale * ( + # prev_health - al_unit.health - al_unit.shield + # ) + # + # for e_id, e_unit in self.enemies.items(): + # if not self.death_tracker_enemy[e_id]: + # prev_health = ( + # self.previous_enemy_units[e_id].health + # + self.previous_enemy_units[e_id].shield + # ) + # if e_unit.health == 0: + # self.death_tracker_enemy[e_id] = 1 + # delta_deaths += self.reward_death_value + # delta_enemy += prev_health + # else: + # delta_enemy += prev_health - e_unit.health - e_unit.shield + # + # if self.reward_only_positive: + # ###### reward = abs(delta_enemy + delta_deaths) # shield regeneration (the original wrong implementation) + # # reward = max(delta_enemy, 0) + delta_deaths # only consider the shield damage + # reward = delta_enemy + delta_deaths # consider the `+shield-damage` and the `-shield-regeneration` + # else: + # reward = delta_enemy + delta_deaths - delta_ally + # + # return reward \ No newline at end of file diff --git a/src/envs/smac_v1/__init__.py b/src/envs/smac_v1/__init__.py new file mode 100644 index 0000000..d35a953 --- /dev/null +++ b/src/envs/smac_v1/__init__.py @@ -0,0 +1 @@ +from .StarCraft2EnvWrapper import StarCraft2EnvWrapper \ No newline at end of file diff --git a/src/envs/smac_v1/official/maps/SMAC_Maps/10m_vs_11m.SC2Map b/src/envs/smac_v1/official/maps/SMAC_Maps/10m_vs_11m.SC2Map new file mode 100644 index 0000000000000000000000000000000000000000..1dc2286dfd39380feafa6f8a1819248c9f2c9e3b GIT binary patch literal 14925 zcmeIZWpHJ=vM#vI%*<3~X1iQwW`;5|Gcz-lnVFfHnVGT7%uHqbx%+hAxN+je%>Ri| z#HZAjGPSg%rBo`VEiEUHJp%v$q`w0IAOJAHJ2L?A&JFW5FjQ^n| z|4;zve}2mW|A+n?`~Q>R_&1*q06;jCl67jisZg$=!?o@=2}{En$IycFftet*ir^ODr*oJmFrYu2xP 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smac_maps + + +def get_map_params(map_name): + map_param_registry = smac_maps.get_smac_map_registry() + return map_param_registry[map_name] diff --git a/src/envs/smac_v1/official/maps/smac_maps.py b/src/envs/smac_v1/official/maps/smac_maps.py new file mode 100644 index 0000000..6c53bc9 --- /dev/null +++ b/src/envs/smac_v1/official/maps/smac_maps.py @@ -0,0 +1,268 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from pysc2.maps import lib + + +class SMACMap(lib.Map): + directory = "SMAC_Maps" + download = "https://github.com/oxwhirl/smac#smac-maps" + players = 2 + step_mul = 8 + game_steps_per_episode = 0 + + +map_param_registry = { +"1m": { + "n_agents": 1, + "n_enemies": 1, + "limit": 30, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "2m": { + "n_agents": 2, + "n_enemies": 2, + "limit": 60, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "4m": { + "n_agents": 4, + "n_enemies": 4, + "limit": 60, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "5m": { + "n_agents": 5, + "n_enemies": 5, + "limit": 60, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "3m": { + "n_agents": 3, + "n_enemies": 3, + "limit": 60, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "8m": { + "n_agents": 8, + "n_enemies": 8, + "limit": 120, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "25m": { + "n_agents": 25, + "n_enemies": 25, + "limit": 150, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "5m_vs_6m": { + "n_agents": 5, + "n_enemies": 6, + "limit": 70, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "8m_vs_9m": { + "n_agents": 8, + "n_enemies": 9, + "limit": 120, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "10m_vs_11m": { + "n_agents": 10, + "n_enemies": 11, + "limit": 150, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "27m_vs_30m": { + "n_agents": 27, + "n_enemies": 30, + "limit": 180, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 0, + "map_type": "marines", + }, + "MMM": { + "n_agents": 10, + "n_enemies": 10, + "limit": 150, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 3, + "map_type": "MMM", + }, + "MMM2": { + "n_agents": 10, + "n_enemies": 12, + "limit": 180, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 3, + "map_type": "MMM", + }, + "2s3z": { + "n_agents": 5, + "n_enemies": 5, + "limit": 120, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 2, + "map_type": "stalkers_and_zealots", + }, + "3s5z": { + "n_agents": 8, + "n_enemies": 8, + "limit": 150, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 2, + "map_type": "stalkers_and_zealots", + }, + "3s5z_vs_3s6z": { + "n_agents": 8, + "n_enemies": 9, + "limit": 170, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 2, + "map_type": "stalkers_and_zealots", + }, + "3s_vs_3z": { + "n_agents": 3, + "n_enemies": 3, + "limit": 150, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 0, + "map_type": "stalkers", + }, + "3s_vs_4z": { + "n_agents": 3, + "n_enemies": 4, + "limit": 200, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 0, + "map_type": "stalkers", + }, + "3s_vs_5z": { + "n_agents": 3, + "n_enemies": 5, + "limit": 250, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 0, + "map_type": "stalkers", + }, + "1c3s5z": { + "n_agents": 9, + "n_enemies": 9, + "limit": 180, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 3, + "map_type": "colossi_stalkers_zealots", + }, + "2m_vs_1z": { + "n_agents": 2, + "n_enemies": 1, + "limit": 150, + "a_race": "T", + "b_race": "P", + "unit_type_bits": 0, + "map_type": "marines", + }, + "corridor": { + "n_agents": 6, + "n_enemies": 24, + "limit": 400, + "a_race": "P", + "b_race": "Z", + "unit_type_bits": 0, + "map_type": "zealots", + }, + "6h_vs_8z": { + "n_agents": 6, + "n_enemies": 8, + "limit": 150, + "a_race": "Z", + "b_race": "P", + "unit_type_bits": 0, + "map_type": "hydralisks", + }, + "2s_vs_1sc": { + "n_agents": 2, + "n_enemies": 1, + "limit": 300, + "a_race": "P", + "b_race": "Z", + "unit_type_bits": 0, + "map_type": "stalkers", + }, + "so_many_baneling": { + "n_agents": 7, + "n_enemies": 32, + "limit": 100, + "a_race": "P", + "b_race": "Z", + "unit_type_bits": 0, + "map_type": "zealots", + }, + "bane_vs_bane": { + "n_agents": 24, + "n_enemies": 24, + "limit": 200, + "a_race": "Z", + "b_race": "Z", + "unit_type_bits": 2, + "map_type": "bane", + }, + "2c_vs_64zg": { + "n_agents": 2, + "n_enemies": 64, + "limit": 400, + "a_race": "P", + "b_race": "Z", + "unit_type_bits": 0, + "map_type": "colossus", + }, +} + + +def get_smac_map_registry(): + return map_param_registry + + +for name in map_param_registry.keys(): + globals()[name] = type(name, (SMACMap,), dict(filename=name)) diff --git a/src/envs/smac_v1/official/starcraft2.py b/src/envs/smac_v1/official/starcraft2.py new file mode 100644 index 0000000..d9ff301 --- /dev/null +++ b/src/envs/smac_v1/official/starcraft2.py @@ -0,0 +1,1699 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from envs.multiagentenv import MultiAgentEnv +from .maps import get_map_params + +import atexit +from warnings import warn +from operator import attrgetter +from copy import deepcopy +import numpy as np +import enum +import math +from absl import logging + +from pysc2 import maps +from pysc2 import run_configs +from pysc2.lib import protocol + +from s2clientprotocol import common_pb2 as sc_common +from s2clientprotocol import sc2api_pb2 as sc_pb +from s2clientprotocol import raw_pb2 as r_pb +from s2clientprotocol import debug_pb2 as d_pb + +races = { + "R": sc_common.Random, + "P": sc_common.Protoss, + "T": sc_common.Terran, + "Z": sc_common.Zerg, +} + +difficulties = { + "1": sc_pb.VeryEasy, + "2": sc_pb.Easy, + "3": sc_pb.Medium, + "4": sc_pb.MediumHard, + "5": sc_pb.Hard, + "6": sc_pb.Harder, + "7": sc_pb.VeryHard, + "8": sc_pb.CheatVision, + "9": sc_pb.CheatMoney, + "A": sc_pb.CheatInsane, +} + +actions = { + "move": 16, # target: PointOrUnit + "attack": 23, # target: PointOrUnit + "stop": 4, # target: None + "heal": 386, # Unit +} + + +class Direction(enum.IntEnum): + NORTH = 0 + SOUTH = 1 + EAST = 2 + WEST = 3 + + +class StarCraft2Env(MultiAgentEnv): + """The StarCraft II environment for decentralised multi-agent + micromanagement scenarios. + """ + + def __init__( + self, + map_name="8m", + step_mul=8, + move_amount=2, + difficulty="7", + game_version=None, + seed=None, + continuing_episode=False, + obs_all_health=True, + obs_own_health=True, + obs_last_action=False, + obs_pathing_grid=False, + obs_terrain_height=False, + obs_instead_of_state=False, + obs_timestep_number=False, + state_last_action=True, + state_timestep_number=False, + reward_sparse=False, + reward_only_positive=True, + reward_death_value=10, + reward_win=200, + reward_defeat=0, + reward_negative_scale=0.5, + reward_scale=True, + reward_scale_rate=20, + replay_dir="", + replay_prefix="", + window_size_x=1920, + window_size_y=1200, + heuristic_ai=False, + heuristic_rest=False, + debug=False, + ): + """ + Create a StarCraftC2Env environment. + + Parameters + ---------- + map_name : str, optional + The name of the SC2 map to play (default is "8m"). The full list + can be found by running bin/map_list. + step_mul : int, optional + How many game steps per agent step (default is 8). None + indicates to use the default map step_mul. + move_amount : float, optional + How far away units are ordered to move per step (default is 2). + difficulty : str, optional + The difficulty of built-in computer AI bot (default is "7"). + game_version : str, optional + StarCraft II game version (default is None). None indicates the + latest version. + seed : int, optional + Random seed used during game initialisation. This allows to + continuing_episode : bool, optional + Whether to consider episodes continuing or finished after time + limit is reached (default is False). + obs_all_health : bool, optional + Agents receive the health of all units (in the sight range) as part + of observations (default is True). + obs_own_health : bool, optional + Agents receive their own health as a part of observations (default + is False). This flag is ignored when obs_all_health == True. + obs_last_action : bool, optional + Agents receive the last actions of all units (in the sight range) + as part of observations (default is False). + obs_pathing_grid : bool, optional + Whether observations include pathing values surrounding the agent + (default is False). + obs_terrain_height : bool, optional + Whether observations include terrain height values surrounding the + agent (default is False). + obs_instead_of_state : bool, optional + Use combination of all agents' observations as the global state + (default is False). + obs_timestep_number : bool, optional + Whether observations include the current timestep of the episode + (default is False). + state_last_action : bool, optional + Include the last actions of all agents as part of the global state + (default is True). + state_timestep_number : bool, optional + Whether the state include the current timestep of the episode + (default is False). + reward_sparse : bool, optional + Receive 1/-1 reward for winning/loosing an episode (default is + False). Whe rest of reward parameters are ignored if True. + reward_only_positive : bool, optional + Reward is always positive (default is True). + reward_death_value : float, optional + The amount of reward received for killing an enemy unit (default + is 10). This is also the negative penalty for having an allied unit + killed if reward_only_positive == False. + reward_win : float, optional + The reward for winning in an episode (default is 200). + reward_defeat : float, optional + The reward for loosing in an episode (default is 0). This value + should be nonpositive. + reward_negative_scale : float, optional + Scaling factor for negative rewards (default is 0.5). This + parameter is ignored when reward_only_positive == True. + reward_scale : bool, optional + Whether or not to scale the reward (default is True). + reward_scale_rate : float, optional + Reward scale rate (default is 20). When reward_scale == True, the + reward received by the agents is divided by (max_reward / + reward_scale_rate), where max_reward is the maximum possible + reward per episode without considering the shield regeneration + of Protoss units. + replay_dir : str, optional + The directory to save replays (default is None). If None, the + replay will be saved in Replays directory where StarCraft II is + installed. + replay_prefix : str, optional + The prefix of the replay to be saved (default is None). If None, + the name of the map will be used. + window_size_x : int, optional + The length of StarCraft II window size (default is 1920). + window_size_y: int, optional + The height of StarCraft II window size (default is 1200). + heuristic_ai: bool, optional + Whether or not to use a non-learning heuristic AI (default False). + heuristic_rest: bool, optional + At any moment, restrict the actions of the heuristic AI to be + chosen from actions available to RL agents (default is False). + Ignored if heuristic_ai == False. + debug: bool, optional + Log messages about observations, state, actions and rewards for + debugging purposes (default is False). + """ + # Map arguments + self.map_name = map_name + map_params = get_map_params(self.map_name) + self.n_agents = map_params["n_agents"] + self.n_enemies = map_params["n_enemies"] + self.episode_limit = map_params["limit"] + self._move_amount = move_amount + self._step_mul = step_mul + self.difficulty = difficulty + + # Observations and state + self.obs_own_health = obs_own_health + self.obs_all_health = obs_all_health + self.obs_instead_of_state = obs_instead_of_state + self.obs_last_action = obs_last_action + self.obs_pathing_grid = obs_pathing_grid + self.obs_terrain_height = obs_terrain_height + self.obs_timestep_number = obs_timestep_number + self.state_last_action = state_last_action + self.state_timestep_number = state_timestep_number + if self.obs_all_health: + self.obs_own_health = True + self.n_obs_pathing = 8 + self.n_obs_height = 9 + + # Rewards args + self.reward_sparse = reward_sparse + self.reward_only_positive = reward_only_positive + self.reward_negative_scale = reward_negative_scale + self.reward_death_value = reward_death_value + self.reward_win = reward_win + self.reward_defeat = reward_defeat + self.reward_scale = reward_scale + self.reward_scale_rate = reward_scale_rate + + # Other + self.game_version = game_version + self.continuing_episode = continuing_episode + self._seed = seed + self.heuristic_ai = heuristic_ai + self.heuristic_rest = heuristic_rest + self.debug = debug + self.window_size = (window_size_x, window_size_y) + self.replay_dir = replay_dir + self.replay_prefix = replay_prefix + + # Actions + self.n_actions_no_attack = 6 + self.n_actions_move = 4 + self.n_actions = self.n_actions_no_attack + self.n_enemies + + # Map info + self._agent_race = map_params["a_race"] + self._bot_race = map_params["b_race"] + self.shield_bits_ally = 1 if self._agent_race == "P" else 0 + self.shield_bits_enemy = 1 if self._bot_race == "P" else 0 + self.unit_type_bits = map_params["unit_type_bits"] + self.map_type = map_params["map_type"] + self._unit_types = None + + self.max_reward = ( + self.n_enemies * self.reward_death_value + self.reward_win + ) + + # create lists containing the names of attributes returned in states + self.ally_state_attr_names = [ + "health", + "energy/cooldown", + "rel_x", + "rel_y", + ] + self.enemy_state_attr_names = ["health", "rel_x", "rel_y"] + + if self.shield_bits_ally > 0: + self.ally_state_attr_names += ["shield"] + if self.shield_bits_enemy > 0: + self.enemy_state_attr_names += ["shield"] + + if self.unit_type_bits > 0: + bit_attr_names = [ + "type_{}".format(bit) for bit in range(self.unit_type_bits) + ] + self.ally_state_attr_names += bit_attr_names + self.enemy_state_attr_names += bit_attr_names + + self.agents = {} + self.enemies = {} + self._episode_count = 0 + self._episode_steps = 0 + self._total_steps = 0 + self._obs = None + self.battles_won = 0 + self.battles_game = 0 + self.timeouts = 0 + self.force_restarts = 0 + self.last_stats = None + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.previous_ally_units = None + self.previous_enemy_units = None + self.last_action = np.zeros((self.n_agents, self.n_actions)) + self._min_unit_type = 0 + self.marine_id = self.marauder_id = self.medivac_id = 0 + self.hydralisk_id = self.zergling_id = self.baneling_id = 0 + self.stalker_id = self.colossus_id = self.zealot_id = 0 + self.max_distance_x = 0 + self.max_distance_y = 0 + self.map_x = 0 + self.map_y = 0 + self.reward = 0 + self.renderer = None + self.terrain_height = None + self.pathing_grid = None + self._run_config = None + self._sc2_proc = None + self._controller = None + + # Try to avoid leaking SC2 processes on shutdown + atexit.register(lambda: self.close()) + + def _launch(self): + """Launch the StarCraft II game.""" + self._run_config = run_configs.get(version=self.game_version) + _map = maps.get(self.map_name) + + # Setting up the interface + interface_options = sc_pb.InterfaceOptions(raw=True, score=False) + self._sc2_proc = self._run_config.start( + window_size=self.window_size, want_rgb=False + ) + self._controller = self._sc2_proc.controller + + # Request to create the game + create = sc_pb.RequestCreateGame( + local_map=sc_pb.LocalMap( + map_path=_map.path, + map_data=self._run_config.map_data(_map.path), + ), + realtime=False, + random_seed=self._seed, + ) + create.player_setup.add(type=sc_pb.Participant) + create.player_setup.add( + type=sc_pb.Computer, + race=races[self._bot_race], + difficulty=difficulties[self.difficulty], + ) + self._controller.create_game(create) + + join = sc_pb.RequestJoinGame( + race=races[self._agent_race], options=interface_options + ) + self._controller.join_game(join) + + game_info = self._controller.game_info() + map_info = game_info.start_raw + map_play_area_min = map_info.playable_area.p0 + map_play_area_max = map_info.playable_area.p1 + self.max_distance_x = map_play_area_max.x - map_play_area_min.x + self.max_distance_y = map_play_area_max.y - map_play_area_min.y + self.map_x = map_info.map_size.x + self.map_y = map_info.map_size.y + + if map_info.pathing_grid.bits_per_pixel == 1: + vals = np.array(list(map_info.pathing_grid.data)).reshape( + self.map_x, int(self.map_y / 8) + ) + self.pathing_grid = np.transpose( + np.array( + [ + [(b >> i) & 1 for b in row for i in range(7, -1, -1)] + for row in vals + ], + dtype=bool, + ) + ) + else: + self.pathing_grid = np.invert( + np.flip( + np.transpose( + np.array( + list(map_info.pathing_grid.data), dtype=np.bool + ).reshape(self.map_x, self.map_y) + ), + axis=1, + ) + ) + + self.terrain_height = ( + np.flip( + np.transpose( + np.array(list(map_info.terrain_height.data)).reshape( + self.map_x, self.map_y + ) + ), + 1, + ) + / 255 + ) + + def reset(self): + """Reset the environment. Required after each full episode. + Returns initial observations and states. + """ + self._episode_steps = 0 + if self._episode_count == 0: + # Launch StarCraft II + self._launch() + else: + self._restart() + + # Information kept for counting the reward + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.previous_ally_units = None + self.previous_enemy_units = None + self.win_counted = False + self.defeat_counted = False + + self.last_action = np.zeros((self.n_agents, self.n_actions)) + + if self.heuristic_ai: + self.heuristic_targets = [None] * self.n_agents + + try: + self._obs = self._controller.observe() + self.init_units() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + if self.debug: + logging.debug( + "Started Episode {}".format(self._episode_count).center( + 60, "*" + ) + ) + + return self.get_obs(), self.get_state() + + def _restart(self): + """Restart the environment by killing all units on the map. + There is a trigger in the SC2Map file, which restarts the + episode when there are no units left. + """ + try: + self._kill_all_units() + self._controller.step(2) + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + def full_restart(self): + """Full restart. Closes the SC2 process and launches a new one.""" + self._sc2_proc.close() + self._launch() + self.force_restarts += 1 + + def step(self, actions): + """A single environment step. Returns reward, terminated, info.""" + actions_int = [int(a) for a in actions] + + self.last_action = np.eye(self.n_actions)[np.array(actions_int)] + + # Collect individual actions + sc_actions = [] + if self.debug: + logging.debug("Actions".center(60, "-")) + + for a_id, action in enumerate(actions_int): + if not self.heuristic_ai: + sc_action = self.get_agent_action(a_id, action) + else: + sc_action, action_num = self.get_agent_action_heuristic( + a_id, action + ) + actions[a_id] = action_num + if sc_action: + sc_actions.append(sc_action) + + # Send action request + req_actions = sc_pb.RequestAction(actions=sc_actions) + try: + self._controller.actions(req_actions) + # Make step in SC2, i.e. apply actions + self._controller.step(self._step_mul) + # Observe here so that we know if the episode is over. + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + return 0, True, {} + + self._total_steps += 1 + self._episode_steps += 1 + + # Update units + game_end_code = self.update_units() + + terminated = False + reward = self.reward_battle() + info = {"battle_won": False} + + # count units that are still alive + dead_allies, dead_enemies = 0, 0 + for _al_id, al_unit in self.agents.items(): + if al_unit.health == 0: + dead_allies += 1 + for _e_id, e_unit in self.enemies.items(): + if e_unit.health == 0: + dead_enemies += 1 + + info["dead_allies"] = dead_allies + info["dead_enemies"] = dead_enemies + + if game_end_code is not None: + # Battle is over + terminated = True + self.battles_game += 1 + if game_end_code == 1 and not self.win_counted: + self.battles_won += 1 + self.win_counted = True + info["battle_won"] = True + if not self.reward_sparse: + reward += self.reward_win + else: + reward = 1 + elif game_end_code == -1 and not self.defeat_counted: + self.defeat_counted = True + if not self.reward_sparse: + reward += self.reward_defeat + else: + reward = -1 + + elif self._episode_steps >= self.episode_limit: + # Episode limit reached + terminated = True + if self.continuing_episode: + info["episode_limit"] = True + self.battles_game += 1 + self.timeouts += 1 + + if self.debug: + logging.debug("Reward = {}".format(reward).center(60, "-")) + + if terminated: + self._episode_count += 1 + + if self.reward_scale: + reward /= self.max_reward / self.reward_scale_rate + + self.reward = reward + + return reward, terminated, info + + def get_agent_action(self, a_id, action): + """Construct the action for agent a_id.""" + avail_actions = self.get_avail_agent_actions(a_id) + assert ( + avail_actions[action] == 1 + ), "Agent {} cannot perform action {}".format(a_id, action) + + unit = self.get_unit_by_id(a_id) + tag = unit.tag + x = unit.pos.x + y = unit.pos.y + + if action == 0: + # no-op (valid only when dead) + assert unit.health == 0, "No-op only available for dead agents." + if self.debug: + logging.debug("Agent {}: Dead".format(a_id)) + return None + elif action == 1: + # stop + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["stop"], + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Stop".format(a_id)) + + elif action == 2: + # move north + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y + self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Move North".format(a_id)) + + elif action == 3: + # move south + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y - self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Move South".format(a_id)) + + elif action == 4: + # move east + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x + self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Move East".format(a_id)) + + elif action == 5: + # move west + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x - self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Move West".format(a_id)) + else: + # attack/heal units that are in range + target_id = action - self.n_actions_no_attack + if self.map_type == "MMM" and unit.unit_type == self.medivac_id: + target_unit = self.agents[target_id] + action_name = "heal" + else: + target_unit = self.enemies[target_id] + action_name = "attack" + + action_id = actions[action_name] + target_tag = target_unit.tag + + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + if self.debug: + logging.debug( + "Agent {} {}s unit # {}".format( + a_id, action_name, target_id + ) + ) + + sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd)) + return sc_action + + def get_agent_action_heuristic(self, a_id, action): + unit = self.get_unit_by_id(a_id) + tag = unit.tag + + target = self.heuristic_targets[a_id] + if unit.unit_type == self.medivac_id: + if ( + target is None + or self.agents[target].health == 0 + or self.agents[target].health == self.agents[target].health_max + ): + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for al_id, al_unit in self.agents.items(): + if al_unit.unit_type == self.medivac_id: + continue + if ( + al_unit.health != 0 + and al_unit.health != al_unit.health_max + ): + dist = self.distance( + unit.pos.x, + unit.pos.y, + al_unit.pos.x, + al_unit.pos.y, + ) + if dist < min_dist: + min_dist = dist + min_id = al_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["heal"] + target_tag = self.agents[self.heuristic_targets[a_id]].tag + else: + if target is None or self.enemies[target].health == 0: + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for e_id, e_unit in self.enemies.items(): + if ( + unit.unit_type == self.marauder_id + and e_unit.unit_type == self.medivac_id + ): + continue + if e_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, e_unit.pos.x, e_unit.pos.y + ) + if dist < min_dist: + min_dist = dist + min_id = e_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["attack"] + target_tag = self.enemies[self.heuristic_targets[a_id]].tag + + action_num = self.heuristic_targets[a_id] + self.n_actions_no_attack + + # Check if the action is available + if ( + self.heuristic_rest + and self.get_avail_agent_actions(a_id)[action_num] == 0 + ): + + # Move towards the target rather than attacking/healing + if unit.unit_type == self.medivac_id: + target_unit = self.agents[self.heuristic_targets[a_id]] + else: + target_unit = self.enemies[self.heuristic_targets[a_id]] + + delta_x = target_unit.pos.x - unit.pos.x + delta_y = target_unit.pos.y - unit.pos.y + + if abs(delta_x) > abs(delta_y): # east or west + if delta_x > 0: # east + target_pos = sc_common.Point2D( + x=unit.pos.x + self._move_amount, y=unit.pos.y + ) + action_num = 4 + else: # west + target_pos = sc_common.Point2D( + x=unit.pos.x - self._move_amount, y=unit.pos.y + ) + action_num = 5 + else: # north or south + if delta_y > 0: # north + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y + self._move_amount + ) + action_num = 2 + else: # south + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y - self._move_amount + ) + action_num = 3 + + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=target_pos, + unit_tags=[tag], + queue_command=False, + ) + else: + # Attack/heal the target + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd)) + return sc_action, action_num + + def reward_battle(self): + """Reward function when self.reward_spare==False. + Returns accumulative hit/shield point damage dealt to the enemy + + reward_death_value per enemy unit killed, and, in case + self.reward_only_positive == False, - (damage dealt to ally units + + reward_death_value per ally unit killed) * self.reward_negative_scale + """ + if self.reward_sparse: + return 0 + + reward = 0 + delta_deaths = 0 + delta_ally = 0 + delta_enemy = 0 + + neg_scale = self.reward_negative_scale + + # update deaths + for al_id, al_unit in self.agents.items(): + if not self.death_tracker_ally[al_id]: + # did not die so far + prev_health = ( + self.previous_ally_units[al_id].health + + self.previous_ally_units[al_id].shield + ) + if al_unit.health == 0: + # just died + self.death_tracker_ally[al_id] = 1 + if not self.reward_only_positive: + delta_deaths -= self.reward_death_value * neg_scale + delta_ally += prev_health * neg_scale + else: + # still alive + delta_ally += neg_scale * ( + prev_health - al_unit.health - al_unit.shield + ) + + for e_id, e_unit in self.enemies.items(): + if not self.death_tracker_enemy[e_id]: + prev_health = ( + self.previous_enemy_units[e_id].health + + self.previous_enemy_units[e_id].shield + ) + if e_unit.health == 0: + self.death_tracker_enemy[e_id] = 1 + delta_deaths += self.reward_death_value + delta_enemy += prev_health + else: + delta_enemy += prev_health - e_unit.health - e_unit.shield + + if self.reward_only_positive: + reward = abs(delta_enemy + delta_deaths) # shield regeneration + else: + reward = delta_enemy + delta_deaths - delta_ally + + return reward + + def get_total_actions(self): + """Returns the total number of actions an agent could ever take.""" + return self.n_actions + + @staticmethod + def distance(x1, y1, x2, y2): + """Distance between two points.""" + return math.hypot(x2 - x1, y2 - y1) + + def unit_shoot_range(self, agent_id): + """Returns the shooting range for an agent.""" + return 6 + + def unit_sight_range(self, agent_id): + """Returns the sight range for an agent.""" + return 9 + + def unit_max_cooldown(self, unit): + """Returns the maximal cooldown for a unit.""" + switcher = { + self.marine_id: 15, + self.marauder_id: 25, + self.medivac_id: 200, # max energy + self.stalker_id: 35, + self.zealot_id: 22, + self.colossus_id: 24, + self.hydralisk_id: 10, + self.zergling_id: 11, + self.baneling_id: 1, + } + return switcher.get(unit.unit_type, 15) + + def save_replay(self): + """Save a replay.""" + prefix = self.replay_prefix or self.map_name + replay_dir = self.replay_dir or "" + replay_path = self._run_config.save_replay( + self._controller.save_replay(), + replay_dir=replay_dir, + prefix=prefix, + ) + logging.info("Replay saved at: %s" % replay_path) + + def unit_max_shield(self, unit): + """Returns maximal shield for a given unit.""" + if unit.unit_type == 74 or unit.unit_type == self.stalker_id: + return 80 # Protoss's Stalker + if unit.unit_type == 73 or unit.unit_type == self.zealot_id: + return 50 # Protoss's Zaelot + if unit.unit_type == 4 or unit.unit_type == self.colossus_id: + return 150 # Protoss's Colossus + + def can_move(self, unit, direction): + """Whether a unit can move in a given direction.""" + m = self._move_amount / 2 + + if direction == Direction.NORTH: + x, y = int(unit.pos.x), int(unit.pos.y + m) + elif direction == Direction.SOUTH: + x, y = int(unit.pos.x), int(unit.pos.y - m) + elif direction == Direction.EAST: + x, y = int(unit.pos.x + m), int(unit.pos.y) + else: + x, y = int(unit.pos.x - m), int(unit.pos.y) + + if self.check_bounds(x, y) and self.pathing_grid[x, y]: + return True + + return False + + def get_surrounding_points(self, unit, include_self=False): + """Returns the surrounding points of the unit in 8 directions.""" + x = int(unit.pos.x) + y = int(unit.pos.y) + + ma = self._move_amount + + points = [ + (x, y + 2 * ma), + (x, y - 2 * ma), + (x + 2 * ma, y), + (x - 2 * ma, y), + (x + ma, y + ma), + (x - ma, y - ma), + (x + ma, y - ma), + (x - ma, y + ma), + ] + + if include_self: + points.append((x, y)) + + return points + + def check_bounds(self, x, y): + """Whether a point is within the map bounds.""" + return 0 <= x < self.map_x and 0 <= y < self.map_y + + def get_surrounding_pathing(self, unit): + """Returns pathing values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=False) + vals = [ + self.pathing_grid[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def get_surrounding_height(self, unit): + """Returns height values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=True) + vals = [ + self.terrain_height[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def get_obs_agent(self, agent_id): + """Returns observation for agent_id. The observation is composed of: + + - agent movement features (where it can move to, height information + and pathing grid) + - enemy features (available_to_attack, health, relative_x, relative_y, + shield, unit_type) + - ally features (visible, distance, relative_x, relative_y, shield, + unit_type) + - agent unit features (health, shield, unit_type) + + All of this information is flattened and concatenated into a list, + in the aforementioned order. To know the sizes of each of the + features inside the final list of features, take a look at the + functions ``get_obs_move_feats_size()``, + ``get_obs_enemy_feats_size()``, ``get_obs_ally_feats_size()`` and + ``get_obs_own_feats_size()``. + + The size of the observation vector may vary, depending on the + environment configuration and type of units present in the map. + For instance, non-Protoss units will not have shields, movement + features may or may not include terrain height and pathing grid, + unit_type is not included if there is only one type of unit in the + map etc.). + + NOTE: Agents should have access only to their local observations + during decentralised execution. + """ + unit = self.get_unit_by_id(agent_id) + + move_feats_dim = self.get_obs_move_feats_size() + enemy_feats_dim = self.get_obs_enemy_feats_size() + ally_feats_dim = self.get_obs_ally_feats_size() + own_feats_dim = self.get_obs_own_feats_size() + + move_feats = np.zeros(move_feats_dim, dtype=np.float32) + enemy_feats = np.zeros(enemy_feats_dim, dtype=np.float32) + ally_feats = np.zeros(ally_feats_dim, dtype=np.float32) + own_feats = np.zeros(own_feats_dim, dtype=np.float32) + + if unit.health > 0: # otherwise dead, return all zeros + x = unit.pos.x + y = unit.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Movement features + avail_actions = self.get_avail_agent_actions(agent_id) + for m in range(self.n_actions_move): + move_feats[m] = avail_actions[m + 2] + + ind = self.n_actions_move + + if self.obs_pathing_grid: + move_feats[ + ind : ind + self.n_obs_pathing # noqa + ] = self.get_surrounding_pathing(unit) + ind += self.n_obs_pathing + + if self.obs_terrain_height: + move_feats[ind:] = self.get_surrounding_height(unit) + + # Enemy features + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + + if ( + dist < sight_range and e_unit.health > 0 + ): # visible and alive + # Sight range > shoot range + enemy_feats[e_id, 0] = avail_actions[ + self.n_actions_no_attack + e_id + ] # available + enemy_feats[e_id, 1] = dist / sight_range # distance + enemy_feats[e_id, 2] = ( + e_x - x + ) / sight_range # relative X + enemy_feats[e_id, 3] = ( + e_y - y + ) / sight_range # relative Y + + ind = 4 + if self.obs_all_health: + enemy_feats[e_id, ind] = ( + e_unit.health / e_unit.health_max + ) # health + ind += 1 + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_feats[e_id, ind] = ( + e_unit.shield / max_shield + ) # shield + ind += 1 + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(e_unit, False) + enemy_feats[e_id, ind + type_id] = 1 # unit type + + # Ally features + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id != agent_id + ] + for i, al_id in enumerate(al_ids): + + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + + if ( + dist < sight_range and al_unit.health > 0 + ): # visible and alive + ally_feats[i, 0] = 1 # visible + ally_feats[i, 1] = dist / sight_range # distance + ally_feats[i, 2] = (al_x - x) / sight_range # relative X + ally_feats[i, 3] = (al_y - y) / sight_range # relative Y + + ind = 4 + if self.obs_all_health: + ally_feats[i, ind] = ( + al_unit.health / al_unit.health_max + ) # health + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_feats[i, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(al_unit, True) + ally_feats[i, ind + type_id] = 1 + ind += self.unit_type_bits + + if self.obs_last_action: + ally_feats[i, ind:] = self.last_action[al_id] + + # Own features + ind = 0 + if self.obs_own_health: + own_feats[ind] = unit.health / unit.health_max + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(unit) + own_feats[ind] = unit.shield / max_shield + ind += 1 + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + own_feats[ind + type_id] = 1 + + agent_obs = np.concatenate( + ( + move_feats.flatten(), + enemy_feats.flatten(), + ally_feats.flatten(), + own_feats.flatten(), + ) + ) + + if self.obs_timestep_number: + agent_obs = np.append( + agent_obs, self._episode_steps / self.episode_limit + ) + + if self.debug: + logging.debug("Obs Agent: {}".format(agent_id).center(60, "-")) + logging.debug( + "Avail. actions {}".format( + self.get_avail_agent_actions(agent_id) + ) + ) + logging.debug("Move feats {}".format(move_feats)) + logging.debug("Enemy feats {}".format(enemy_feats)) + logging.debug("Ally feats {}".format(ally_feats)) + logging.debug("Own feats {}".format(own_feats)) + + return agent_obs + + def get_obs(self): + """Returns all agent observations in a list. + NOTE: Agents should have access only to their local observations + during decentralised execution. + """ + agents_obs = [self.get_obs_agent(i) for i in range(self.n_agents)] + return agents_obs + + def get_state(self): + """Returns the global state. + NOTE: This functon should not be used during decentralised execution. + """ + if self.obs_instead_of_state: + obs_concat = np.concatenate(self.get_obs(), axis=0).astype( + np.float32 + ) + return obs_concat + + state_dict = self.get_state_dict() + + state = np.append( + state_dict["allies"].flatten(), state_dict["enemies"].flatten() + ) + if "last_action" in state_dict: + state = np.append(state, state_dict["last_action"].flatten()) + if "timestep" in state_dict: + state = np.append(state, state_dict["timestep"]) + + state = state.astype(dtype=np.float32) + + if self.debug: + logging.debug("STATE".center(60, "-")) + logging.debug("Ally state {}".format(state_dict["allies"])) + logging.debug("Enemy state {}".format(state_dict["enemies"])) + if self.state_last_action: + logging.debug("Last actions {}".format(self.last_action)) + + return state + + def get_ally_num_attributes(self): + return len(self.ally_state_attr_names) + + def get_enemy_num_attributes(self): + return len(self.enemy_state_attr_names) + + def get_state_dict(self): + """Returns the global state as a dictionary. + + - allies: numpy array containing agents and their attributes + - enemies: numpy array containing enemies and their attributes + - last_action: numpy array of previous actions for each agent + - timestep: current no. of steps divided by total no. of steps + + NOTE: This function should not be used during decentralised execution. + """ + + # number of features equals the number of attribute names + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + ally_state = np.zeros((self.n_agents, nf_al)) + enemy_state = np.zeros((self.n_enemies, nf_en)) + + center_x = self.map_x / 2 + center_y = self.map_y / 2 + + for al_id, al_unit in self.agents.items(): + if al_unit.health > 0: + x = al_unit.pos.x + y = al_unit.pos.y + max_cd = self.unit_max_cooldown(al_unit) + + ally_state[al_id, 0] = ( + al_unit.health / al_unit.health_max + ) # health + if ( + self.map_type == "MMM" + and al_unit.unit_type == self.medivac_id + ): + ally_state[al_id, 1] = al_unit.energy / max_cd # energy + else: + ally_state[al_id, 1] = ( + al_unit.weapon_cooldown / max_cd + ) # cooldown + ally_state[al_id, 2] = ( + x - center_x + ) / self.max_distance_x # relative X + ally_state[al_id, 3] = ( + y - center_y + ) / self.max_distance_y # relative Y + + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_state[al_id, 4] = ( + al_unit.shield / max_shield + ) # shield + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(al_unit, True) + ally_state[al_id, type_id - self.unit_type_bits] = 1 + + for e_id, e_unit in self.enemies.items(): + if e_unit.health > 0: + x = e_unit.pos.x + y = e_unit.pos.y + + enemy_state[e_id, 0] = ( + e_unit.health / e_unit.health_max + ) # health + enemy_state[e_id, 1] = ( + x - center_x + ) / self.max_distance_x # relative X + enemy_state[e_id, 2] = ( + y - center_y + ) / self.max_distance_y # relative Y + + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_state[e_id, 3] = e_unit.shield / max_shield # shield + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(e_unit, False) + enemy_state[e_id, type_id - self.unit_type_bits] = 1 + + state = {"allies": ally_state, "enemies": enemy_state} + + if self.state_last_action: + state["last_action"] = self.last_action + if self.state_timestep_number: + state["timestep"] = self._episode_steps / self.episode_limit + + return state + + def get_obs_enemy_feats_size(self): + """Returns the dimensions of the matrix containing enemy features. + Size is n_enemies x n_features. + """ + nf_en = 4 + self.unit_type_bits + + if self.obs_all_health: + nf_en += 1 + self.shield_bits_enemy + + return self.n_enemies, nf_en + + def get_obs_ally_feats_size(self): + """Returns the dimensions of the matrix containing ally features. + Size is n_allies x n_features. + """ + nf_al = 4 + self.unit_type_bits + + if self.obs_all_health: + nf_al += 1 + self.shield_bits_ally + + if self.obs_last_action: + nf_al += self.n_actions + + return self.n_agents - 1, nf_al + + def get_obs_own_feats_size(self): + """ + Returns the size of the vector containing the agents' own features. + """ + own_feats = self.unit_type_bits + if self.obs_own_health: + own_feats += 1 + self.shield_bits_ally + if self.obs_timestep_number: + own_feats += 1 + + return own_feats + + def get_obs_move_feats_size(self): + """Returns the size of the vector containing the agents's movement- + related features. + """ + move_feats = self.n_actions_move + if self.obs_pathing_grid: + move_feats += self.n_obs_pathing + if self.obs_terrain_height: + move_feats += self.n_obs_height + + return move_feats + + def get_obs_size(self): + """Returns the size of the observation.""" + own_feats = self.get_obs_own_feats_size() + move_feats = self.get_obs_move_feats_size() + + n_enemies, n_enemy_feats = self.get_obs_enemy_feats_size() + n_allies, n_ally_feats = self.get_obs_ally_feats_size() + + enemy_feats = n_enemies * n_enemy_feats + ally_feats = n_allies * n_ally_feats + + return move_feats + enemy_feats + ally_feats + own_feats + + def get_state_size(self): + """Returns the size of the global state.""" + if self.obs_instead_of_state: + return self.get_obs_size() * self.n_agents + + nf_al = 4 + self.shield_bits_ally + self.unit_type_bits + nf_en = 3 + self.shield_bits_enemy + self.unit_type_bits + + enemy_state = self.n_enemies * nf_en + ally_state = self.n_agents * nf_al + + size = enemy_state + ally_state + + if self.state_last_action: + size += self.n_agents * self.n_actions + if self.state_timestep_number: + size += 1 + + return size + + def get_visibility_matrix(self): + """Returns a boolean numpy array of dimensions + (n_agents, n_agents + n_enemies) indicating which units + are visible to each agent. + """ + arr = np.zeros( + (self.n_agents, self.n_agents + self.n_enemies), + dtype=np.bool, + ) + + for agent_id in range(self.n_agents): + current_agent = self.get_unit_by_id(agent_id) + if current_agent.health > 0: # it agent not dead + x = current_agent.pos.x + y = current_agent.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Enemies + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + + if dist < sight_range and e_unit.health > 0: + # visible and alive + arr[agent_id, self.n_agents + e_id] = 1 + + # The matrix for allies is filled symmetrically + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id > agent_id + ] + for _, al_id in enumerate(al_ids): + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + + if dist < sight_range and al_unit.health > 0: + # visible and alive + arr[agent_id, al_id] = arr[al_id, agent_id] = 1 + + return arr + + def get_unit_type_id(self, unit, ally): + """Returns the ID of unit type in the given scenario.""" + if ally: # use new SC2 unit types + type_id = unit.unit_type - self._min_unit_type + else: # use default SC2 unit types + if self.map_type == "stalkers_and_zealots": + # id(Stalker) = 74, id(Zealot) = 73 + type_id = unit.unit_type - 73 + elif self.map_type == "colossi_stalkers_zealots": + # id(Stalker) = 74, id(Zealot) = 73, id(Colossus) = 4 + if unit.unit_type == 4: + type_id = 0 + elif unit.unit_type == 74: + type_id = 1 + else: + type_id = 2 + elif self.map_type == "bane": + if unit.unit_type == 9: + type_id = 0 + else: + type_id = 1 + elif self.map_type == "MMM": + if unit.unit_type == 51: + type_id = 0 + elif unit.unit_type == 48: + type_id = 1 + else: + type_id = 2 + + return type_id + + def get_avail_agent_actions(self, agent_id): + """Returns the available actions for agent_id.""" + unit = self.get_unit_by_id(agent_id) + if unit.health > 0: + # cannot choose no-op when alive + avail_actions = [0] * self.n_actions + + # stop should be allowed + avail_actions[1] = 1 + + # see if we can move + if self.can_move(unit, Direction.NORTH): + avail_actions[2] = 1 + if self.can_move(unit, Direction.SOUTH): + avail_actions[3] = 1 + if self.can_move(unit, Direction.EAST): + avail_actions[4] = 1 + if self.can_move(unit, Direction.WEST): + avail_actions[5] = 1 + + # Can attack only alive units that are alive in the shooting range + shoot_range = self.unit_shoot_range(agent_id) + + target_items = self.enemies.items() + if self.map_type == "MMM" and unit.unit_type == self.medivac_id: + # Medivacs cannot heal themselves or other flying units + target_items = [ + (t_id, t_unit) + for (t_id, t_unit) in self.agents.items() + if t_unit.unit_type != self.medivac_id + ] + + for t_id, t_unit in target_items: + if t_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, t_unit.pos.x, t_unit.pos.y + ) + if dist <= shoot_range: + avail_actions[t_id + self.n_actions_no_attack] = 1 + + return avail_actions + + else: + # only no-op allowed + return [1] + [0] * (self.n_actions - 1) + + def get_avail_actions(self): + """Returns the available actions of all agents in a list.""" + avail_actions = [] + for agent_id in range(self.n_agents): + avail_agent = self.get_avail_agent_actions(agent_id) + avail_actions.append(avail_agent) + return avail_actions + + def close(self): + """Close StarCraft II.""" + if self.renderer is not None: + self.renderer.close() + self.renderer = None + if self._sc2_proc: + self._sc2_proc.close() + + def seed(self): + """Returns the random seed used by the environment.""" + return self._seed + + def render(self, mode="human"): + if self.renderer is None: + from smac.env.starcraft2.render import StarCraft2Renderer + + self.renderer = StarCraft2Renderer(self, mode) + assert ( + mode == self.renderer.mode + ), "mode must be consistent across render calls" + return self.renderer.render(mode) + + def _kill_all_units(self): + """Kill all units on the map.""" + units_alive = [ + unit.tag for unit in self.agents.values() if unit.health > 0 + ] + [unit.tag for unit in self.enemies.values() if unit.health > 0] + debug_command = [ + d_pb.DebugCommand(kill_unit=d_pb.DebugKillUnit(tag=units_alive)) + ] + self._controller.debug(debug_command) + + def init_units(self): + """Initialise the units.""" + while True: + # Sometimes not all units have yet been created by SC2 + self.agents = {} + self.enemies = {} + + ally_units = [ + unit + for unit in self._obs.observation.raw_data.units + if unit.owner == 1 + ] + ally_units_sorted = sorted( + ally_units, + key=attrgetter("unit_type", "pos.x", "pos.y"), + reverse=False, + ) + + for i in range(len(ally_units_sorted)): + self.agents[i] = ally_units_sorted[i] + if self.debug: + logging.debug( + "Unit {} is {}, x = {}, y = {}".format( + len(self.agents), + self.agents[i].unit_type, + self.agents[i].pos.x, + self.agents[i].pos.y, + ) + ) + + for unit in self._obs.observation.raw_data.units: + if unit.owner == 2: + self.enemies[len(self.enemies)] = unit + if self._episode_count == 0: + self.max_reward += unit.health_max + unit.shield_max + + if self._episode_count == 0: + min_unit_type = min( + unit.unit_type for unit in self.agents.values() + ) + self._init_ally_unit_types(min_unit_type) + + all_agents_created = len(self.agents) == self.n_agents + all_enemies_created = len(self.enemies) == self.n_enemies + + self._unit_types = [ + unit.unit_type for unit in ally_units_sorted + ] + [ + unit.unit_type + for unit in self._obs.observation.raw_data.units + if unit.owner == 2 + ] + + if all_agents_created and all_enemies_created: # all good + return + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset() + + def get_unit_types(self): + if self._unit_types is None: + warn( + "unit types have not been initialized yet, please call" + "env.reset() to populate this and call t1286he method again." + ) + + return self._unit_types + + def update_units(self): + """Update units after an environment step. + This function assumes that self._obs is up-to-date. + """ + n_ally_alive = 0 + n_enemy_alive = 0 + + # Store previous state + self.previous_ally_units = deepcopy(self.agents) + self.previous_enemy_units = deepcopy(self.enemies) + + for al_id, al_unit in self.agents.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if al_unit.tag == unit.tag: + self.agents[al_id] = unit + updated = True + n_ally_alive += 1 + break + + if not updated: # dead + al_unit.health = 0 + + for e_id, e_unit in self.enemies.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if e_unit.tag == unit.tag: + self.enemies[e_id] = unit + updated = True + n_enemy_alive += 1 + break + + if not updated: # dead + e_unit.health = 0 + + if ( + n_ally_alive == 0 + and n_enemy_alive > 0 + or self.only_medivac_left(ally=True) + ): + return -1 # lost + if ( + n_ally_alive > 0 + and n_enemy_alive == 0 + or self.only_medivac_left(ally=False) + ): + return 1 # won + if n_ally_alive == 0 and n_enemy_alive == 0: + return 0 + + return None + + def _init_ally_unit_types(self, min_unit_type): + """Initialise ally unit types. Should be called once from the + init_units function. + """ + self._min_unit_type = min_unit_type + if self.map_type == "marines": + self.marine_id = min_unit_type + elif self.map_type == "stalkers_and_zealots": + self.stalker_id = min_unit_type + self.zealot_id = min_unit_type + 1 + elif self.map_type == "colossi_stalkers_zealots": + self.colossus_id = min_unit_type + self.stalker_id = min_unit_type + 1 + self.zealot_id = min_unit_type + 2 + elif self.map_type == "MMM": + self.marauder_id = min_unit_type + self.marine_id = min_unit_type + 1 + self.medivac_id = min_unit_type + 2 + elif self.map_type == "zealots": + self.zealot_id = min_unit_type + elif self.map_type == "hydralisks": + self.hydralisk_id = min_unit_type + elif self.map_type == "stalkers": + self.stalker_id = min_unit_type + elif self.map_type == "colossus": + self.colossus_id = min_unit_type + elif self.map_type == "bane": + self.baneling_id = min_unit_type + self.zergling_id = min_unit_type + 1 + + def only_medivac_left(self, ally): + """Check if only Medivac units are left.""" + if self.map_type != "MMM": + return False + + if ally: + units_alive = [ + a + for a in self.agents.values() + if (a.health > 0 and a.unit_type != self.medivac_id) + ] + if len(units_alive) == 0: + return True + return False + else: + units_alive = [ + a + for a in self.enemies.values() + if (a.health > 0 and a.unit_type != self.medivac_id) + ] + if len(units_alive) == 1 and units_alive[0].unit_type == 54: + return True + return False + + def get_unit_by_id(self, a_id): + """Get unit by ID.""" + return self.agents[a_id] + + def get_stats(self): + stats = { + "battles_won": self.battles_won, + "battles_game": self.battles_game, + "battles_draw": self.timeouts, + "win_rate": self.battles_won / self.battles_game, + "timeouts": self.timeouts, + "restarts": self.force_restarts, + } + return stats + + def get_env_info(self): + env_info = super().get_env_info() + env_info["agent_features"] = self.ally_state_attr_names + env_info["enemy_features"] = self.enemy_state_attr_names + return env_info \ No newline at end of file diff --git a/src/envs/smac_v2/README_tju.md b/src/envs/smac_v2/README_tju.md new file mode 100644 index 0000000..e59ee40 --- /dev/null +++ b/src/envs/smac_v2/README_tju.md @@ -0,0 +1,5 @@ + +# Instructions + +## Firstly, install SMACv2 according to https://github.com/oxwhirl/smacv2 +* Note that make sure to put the maps in `smac/env/starcraft2/maps/SMAC_Maps` to diff --git a/src/envs/smac_v2/StarCraft2Env2Wrapper.py b/src/envs/smac_v2/StarCraft2Env2Wrapper.py new file mode 100644 index 0000000..21fbf21 --- /dev/null +++ b/src/envs/smac_v2/StarCraft2Env2Wrapper.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +@Project ：API-Network +@File ：StarCraft2EnvWrapper.py +@Author ：Hao Xiaotian +@Date ：2022/6/13 16:26 +''' + +from .official.wrapper import StarCraftCapabilityEnvWrapper + + +class StarCraft2Env2Wrapper(StarCraftCapabilityEnvWrapper): + + # Add new functions to support permutation operation + def get_obs_component(self): + move_feats_dim = self.env.get_obs_move_feats_size() + enemy_feats_dim = self.env.get_obs_enemy_feats_size() + ally_feats_dim = self.env.get_obs_ally_feats_size() + own_feats_dim = self.env.get_obs_own_feats_size() + obs_component = [move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim] + return obs_component + + def get_state_component(self): + if self.env.obs_instead_of_state: + return [self.env.get_obs_size()] * self.env.n_agents + + nf_al = self.env.get_ally_num_attributes() + nf_en = self.env.get_enemy_num_attributes() + + enemy_state = self.env.n_enemies * nf_en + ally_state = self.env.n_agents * nf_al + + size = [ally_state, enemy_state] + + if self.env.state_last_action: + size.append(self.env.n_agents * self.env.n_actions) + if self.env.state_timestep_number: + size.append(1) + return size + + def get_env_info(self): + env_info = { + "state_shape": self.get_state_size(), + "obs_shape": self.get_obs_size(), + "n_actions": self.get_total_actions(), + "n_agents": self.env.n_agents, + "n_enemies": self.env.n_enemies, + "episode_limit": self.env.episode_limit, + + # New features we added. + "n_normal_actions": self.env.n_actions_no_attack, + "n_allies": self.env.n_agents - 1, + "state_ally_feats_size": self.env.get_ally_num_attributes(), + "state_enemy_feats_size": self.env.get_enemy_num_attributes(), + "obs_component": self.get_obs_component(), + "state_component": self.get_state_component(), + "map_type": self.env.map_type, + } + print(env_info) + return env_info + + def _get_medivac_ids(self): + medivac_ids = [] + for al_id, al_unit in self.env.agents.items(): + if self.env.map_type == "MMM" and al_unit.unit_type == self.env.medivac_id: + medivac_ids.append(al_id) + print(medivac_ids) # [9] + return medivac_ids + + # def reward_battle(self): + # """Reward function when self.reward_spare==False. + # + # Fix the **REWARD FUNCTION BUG** of the original starcraft2.py. + # + # We carefully check the code and indeed find some code error in starcraft2.py. + # The error is caused by the incorrect reward calculation for the shield regeneration process and this error will + # only occur for scenarios where the enemies are Protoss units. + # + # (1) At line 717 of reward_battle() of starcraft2.py, the reward is computed as: reward = abs(delta_enemy). + # Normally, when the agents attack the enemies, delta_enemy will > 0 and thus the agents will be rewarded for attacking enemies. + # + # (2) For Protoss enemies, delta_enemy can < 0 due to the shield regeneration. However, due to the abs() taken over delta_enemy, + # the agents will still be rewarded when the enemies' shields regenerate. This incorrect reward will lead to undesired behaviors, + # e.g., attacking the enemies but not killing them and waiting their shields regenerating. + # + # (3) Due to the PI/PE design and the improved representational capacity, HPN-QMIX is more sensitive to such + # incorrect rewards and sometimes learn strange behaviors. + # + # Returns accumulative hit/shield point damage dealt to the enemy + # + reward_death_value per enemy unit killed, and, in case + # self.reward_only_positive == False, - (damage dealt to ally units + # + reward_death_value per ally unit killed) * self.reward_negative_scale + # """ + # if self.reward_sparse: + # return 0 + # + # reward = 0 + # delta_deaths = 0 + # delta_ally = 0 + # delta_enemy = 0 + # + # neg_scale = self.reward_negative_scale + # + # # update deaths + # for al_id, al_unit in self.agents.items(): + # if not self.death_tracker_ally[al_id]: + # # did not die so far + # prev_health = ( + # self.previous_ally_units[al_id].health + # + self.previous_ally_units[al_id].shield + # ) + # if al_unit.health == 0: + # # just died + # self.death_tracker_ally[al_id] = 1 + # if not self.reward_only_positive: + # delta_deaths -= self.reward_death_value * neg_scale + # delta_ally += prev_health * neg_scale + # else: + # # still alive + # delta_ally += neg_scale * ( + # prev_health - al_unit.health - al_unit.shield + # ) + # + # for e_id, e_unit in self.enemies.items(): + # if not self.death_tracker_enemy[e_id]: + # prev_health = ( + # self.previous_enemy_units[e_id].health + # + self.previous_enemy_units[e_id].shield + # ) + # if e_unit.health == 0: + # self.death_tracker_enemy[e_id] = 1 + # delta_deaths += self.reward_death_value + # delta_enemy += prev_health + # else: + # delta_enemy += prev_health - e_unit.health - e_unit.shield + # + # if self.reward_only_positive: + # ###### reward = abs(delta_enemy + delta_deaths) # shield regeneration (the original wrong implementation) + # # reward = max(delta_enemy, 0) + delta_deaths # only consider the shield damage + # reward = delta_enemy + delta_deaths # consider the `+shield-damage` and the `-shield-regeneration` + # else: + # reward = delta_enemy + delta_deaths - delta_ally + # + # return reward diff --git a/src/envs/smac_v2/__init__.py b/src/envs/smac_v2/__init__.py new file mode 100644 index 0000000..72364d0 --- /dev/null +++ b/src/envs/smac_v2/__init__.py @@ -0,0 +1 @@ +from .StarCraft2Env2Wrapper import StarCraft2Env2Wrapper \ No newline at end of file diff --git a/src/envs/smac_v2/official/__init__.py b/src/envs/smac_v2/official/__init__.py new file mode 100644 index 0000000..4e7fdbf --- /dev/null +++ b/src/envs/smac_v2/official/__init__.py @@ -0,0 +1,4 @@ +from absl import flags + +FLAGS = flags.FLAGS +FLAGS(["main.py"]) diff --git a/src/envs/smac_v2/official/distributions.py b/src/envs/smac_v2/official/distributions.py new file mode 100644 index 0000000..9d08f82 --- /dev/null +++ b/src/envs/smac_v2/official/distributions.py @@ -0,0 +1,329 @@ +from abc import ABC, abstractmethod, abstractproperty +from copy import deepcopy +from typing import Any, Dict +from itertools import combinations_with_replacement +from random import choice, shuffle +from math import inf +from numpy.random import default_rng +import numpy as np + + +class Distribution(ABC): + @abstractmethod + def generate(self) -> Dict[str, Any]: + pass + + @property + @abstractproperty + def n_tasks(self) -> int: + pass + + +DISTRIBUTION_MAP = {} + + +def get_distribution(key): + return DISTRIBUTION_MAP[key] + + +def register_distribution(key, cls): + DISTRIBUTION_MAP[key] = cls + + +class FixedDistribution(Distribution): + """A generic disribution that draws from a fixed list. + May operate in test mode, where items are drawn sequentially, + or train mode where items are drawn randomly. Example uses of this + are for team generation or per-agent accuracy generation in SMAC by + drawing from separate fixed lists at test and train time. + """ + + def __init__(self, config): + """ + Args: + config (dict): Must contain `env_key`, `test_mode` and `items` + entries. `env_key` is the key to pass to the environment so that it + recognises what to do with the list. `test_mode` controls the sampling + behaviour (sequential if true, uniform at random if false), `items` + is the list of items (team configurations/accuracies etc.) to sample from. + """ + self.config = config + self.env_key = config["env_key"] + self.test_mode = config["test_mode"] + self.teams = config["items"] + self.index = 0 + + def generate(self) -> Dict[str, Dict[str, Any]]: + """Returns: + Dict: Returns a dict of the form + {self.env_key: {"item": , "id": }} + """ + if self.test_mode: + team = self.teams[self.index] + team_id = self.index + self.index = (self.index + 1) % len(self.teams) + shuffle(team) + return {self.env_key: {"item": team, "id": team_id}} + else: + team = choice(self.teams) + team_id = self.teams.index(team) + shuffle(team) + return {self.env_key: {"item": team, "id": team_id}} + + @property + def n_tasks(self): + return len(self.teams) + + +register_distribution("fixed", FixedDistribution) + + +class AllTeamsDistribution(Distribution): + def __init__(self, config): + self.config = config + self.units = config["unit_types"] + self.n_units = config["n_units"] + self.exceptions = config.get("exception_unit_types", []) + self.combinations = list( + combinations_with_replacement(self.units, self.n_units) + ) + + def generate(self) -> Dict[str, Dict[str, Any]]: + team = [] + while not team or all(member in self.exceptions for member in team): + team = list(choice(self.combinations)) + team_id = self.combinations.index(tuple(team)) + shuffle(team) + return {"team_gen": {"item": team, "id": team_id}} + + @property + def n_tasks(self): + # TODO adjust so that this can handle exceptions + assert not self.exceptions + return len(self.combinations) + + +register_distribution("all_teams", AllTeamsDistribution) + + +class WeightedTeamsDistribution(Distribution): + def __init__(self, config): + self.config = config + self.units = np.array(config["unit_types"]) + self.n_units = config["n_units"] + self.weights = np.array(config["weights"]) + self.exceptions = config.get("exception_unit_types", set()) + self.rng = default_rng() + + def generate(self) -> Dict[str, Dict[str, Any]]: + team = [] + while not team or all(member in self.exceptions for member in team): + team = list( + self.rng.choice( + self.units, size=(self.n_units,), p=self.weights + ) + ) + shuffle(team) + return {"team_gen": {"item": team, "id": 0}} + + @property + def n_tasks(self): + return inf + + +register_distribution("weighted_teams", WeightedTeamsDistribution) + + +class PerAgentUniformDistribution(Distribution): + """A generic distribution for generating some information per-agent drawn + from a uniform distribution in a specified range. + """ + + def __init__(self, config): + self.config = config + self.lower_bound = config["lower_bound"] + self.upper_bound = config["upper_bound"] + self.env_key = config["env_key"] + self.n_units = config["n_units"] + self.rng = default_rng() + + def generate(self) -> Dict[str, Dict[str, Any]]: + probs = self.rng.uniform( + low=self.lower_bound, + high=self.upper_bound, + size=(self.n_units, len(self.lower_bound)), + ) + return {self.env_key: {"item": probs, "id": 0}} + + @property + def n_tasks(self): + return inf + + +register_distribution("per_agent_uniform", PerAgentUniformDistribution) + + +class MaskDistribution(Distribution): + def __init__(self, config: Dict[str, Any]): + self.config = config + self.mask_probability = config["mask_probability"] + self.n_units = config["n_units"] + self.n_enemies = config["n_enemies"] + self.rng = default_rng() + + def generate(self) -> Dict[str, Dict[str, Any]]: + mask = self.rng.choice( + [0, 1], + size=(self.n_units, self.n_enemies), + p=[ + self.mask_probability, + 1.0 - self.mask_probability, + ], + ) + return {"enemy_mask": {"item": mask, "id": 0}} + + @property + def n_tasks(self): + return inf + + +register_distribution("mask", MaskDistribution) + + +class ReflectPositionDistribution(Distribution): + """Distribution that will generate enemy and ally + positions. Generates ally positions uniformly at + random and then reflects these in a vertical line + half-way across the map to get the enemy positions. + Only works when the number of agents and enemies is the same. + """ + + def __init__(self, config): + self.config = config + self.n_units = config["n_units"] + self.map_x = config["map_x"] + self.map_y = config["map_y"] + config_copy = deepcopy(config) + config_copy["env_key"] = "ally_start_positions" + config_copy["lower_bound"] = (0, 0) + # subtract one from the x coordinate because SC2 goes wrong + # when you spawn ally and enemy units on top of one another + # -1 gives a sensible 'buffer zone' of size 2 + config_copy["upper_bound"] = (self.map_x / 2 - 1, self.map_y) + self.pos_generator = PerAgentUniformDistribution(config_copy) + + def generate(self) -> Dict[str, Dict[str, Any]]: + ally_positions_dict = self.pos_generator.generate() + ally_positions = ally_positions_dict["ally_start_positions"]["item"] + enemy_positions = np.zeros_like(ally_positions) + enemy_positions[:, 0] = self.map_x - ally_positions[:, 0] + enemy_positions[:, 1] = ally_positions[:, 1] + return { + "ally_start_positions": {"item": ally_positions, "id": 0}, + "enemy_start_positions": {"item": enemy_positions, "id": 0}, + } + + @property + def n_tasks(self) -> int: + return inf + + +register_distribution("reflect_position", ReflectPositionDistribution) + + +class SurroundedPositionDistribution(Distribution): + """Distribution that generates ally positions in a + circle at the centre of the map, and then has enemies + randomly distributed in the four diagonal directions at a + random distance. + """ + + def __init__(self, config): + self.config = config + self.n_units = config["n_units"] + self.n_enemies = config["n_enemies"] + self.map_x = config["map_x"] + self.map_y = config["map_y"] + self.rng = default_rng() + + def generate(self) -> Dict[str, Dict[str, Any]]: + # need multiple centre points because SC2 does not cope with + # spawning ally and enemy units on top of one another in some + # cases + offset = 2 + centre_point = np.array([self.map_x / 2, self.map_y / 2]) + diagonal_to_centre_point = { + 0: np.array([self.map_x / 2 - offset, self.map_y / 2 - offset]), + 1: np.array([self.map_x / 2 - offset, self.map_y / 2 + offset]), + 2: np.array([self.map_x / 2 + offset, self.map_y / 2 + offset]), + 3: np.array([self.map_x / 2 + offset, self.map_y / 2 - offset]), + } + ally_position = np.tile(centre_point, (self.n_units, 1)) + enemy_position = np.zeros((self.n_enemies, 2)) + # decide on the number of groups (between 1 and 4) + n_groups = self.rng.integers(1, 5) + # generate the number of enemies in each group + group_membership = self.rng.multinomial( + self.n_enemies, np.ones(n_groups) / n_groups + ) + # decide on the distance along the diagonal for each group + group_position = self.rng.uniform(size=(n_groups,)) + group_diagonals = self.rng.choice( + np.array(range(4)), size=(n_groups,), replace=False + ) + + diagonal_to_point_map = { + 0: np.array([0, 0]), + 1: np.array([0, self.map_y]), + 2: np.array([self.map_x, self.map_y]), + 3: np.array([self.map_x, 0]), + } + unit_index = 0 + for i in range(n_groups): + t = group_position[i] + enemy_position[ + unit_index : unit_index + group_membership[i], : + ] = diagonal_to_centre_point[ + group_diagonals[i] + ] * t + diagonal_to_point_map[ + group_diagonals[i] + ] * ( + 1 - t + ) + + return { + "ally_start_positions": {"item": ally_position, "id": 0}, + "enemy_start_positions": {"item": enemy_position, "id": 0}, + } + + @property + def n_tasks(self): + return inf + + +register_distribution("surrounded", SurroundedPositionDistribution) + +# If this becomes common, then should work on a more satisfying way +# of doing this +class SurroundedAndReflectPositionDistribution(Distribution): + def __init__(self, config): + self.p_threshold = config["p"] + self.surrounded_distribution = SurroundedPositionDistribution(config) + self.reflect_distribution = ReflectPositionDistribution(config) + self.rng = default_rng() + + def generate(self) -> Dict[str, Dict[str, Any]]: + p = self.rng.random() + if p > self.p_threshold: + return self.surrounded_distribution.generate() + else: + return self.reflect_distribution.generate() + + @property + def n_tasks(self): + return inf + + +register_distribution( + "surrounded_and_reflect", SurroundedAndReflectPositionDistribution +) diff --git a/src/envs/smac_v2/official/maps/SMAC_Maps/10gen_empty.SC2Map b/src/envs/smac_v2/official/maps/SMAC_Maps/10gen_empty.SC2Map new file mode 100644 index 0000000000000000000000000000000000000000..f5bece9e4bb7382851cf1b6fdb7b35c0b74a0a36 GIT binary patch literal 26384 zcmeFYWpE}tvnKeOnW4?h%*@QpZDwX>W@fk9ZDwX>X6Clr%*^)q-0$3&I}`h3e{ZZJ z9;Kv`Qp!|SWhteSRZzs51pokuW&i*P01VKp4*>KU0{{ZwWu)KupW&Z6{{&0j|Ik&P z|4;zvf2acRzxjVj;J;Mtzsm&x0OZU@-rAd=Lbn&Nf}N#WoB10nSuWze*Y{gnP~1r< zJO3vK06+<2yZi@uuT&&1jR$w-`2zI1R=$;5$p?no$a0KN$Guxd_nbMSPu; 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smac_maps + + +def get_map_params(map_name): + map_param_registry = smac_maps.get_smac_map_registry() + return map_param_registry[map_name] diff --git a/src/envs/smac_v2/official/maps/smac_maps.py b/src/envs/smac_v2/official/maps/smac_maps.py new file mode 100644 index 0000000..aa3c106 --- /dev/null +++ b/src/envs/smac_v2/official/maps/smac_maps.py @@ -0,0 +1,58 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from pysc2.maps import lib + + + +class SMACMap(lib.Map): + directory = "SMAC_Maps" + download = "https://github.com/oxwhirl/smac#smac-maps" + players = 2 + step_mul = 8 + game_steps_per_episode = 0 + + +map_param_registry = { + "10gen_terran": { + "n_agents": 10, + "n_enemies": 10, + "limit": 400, + "a_race": "T", + "b_race": "T", + "unit_type_bits": 3, + "map_type": "terran_gen", + "map_name": "32x32_flat", + }, + "10gen_zerg": { + "n_agents": 10, + "n_enemies": 10, + "limit": 400, + "a_race": "Z", + "b_race": "Z", + "unit_type_bits": 3, + "map_type": "zerg_gen", + "map_name": "32x32_flat", + }, + "10gen_protoss": { + "n_agents": 10, + "n_enemies": 10, + "limit": 400, + "a_race": "P", + "b_race": "P", + "unit_type_bits": 3, + "map_type": "protoss_gen", + "map_name": "32x32_flat", + }, +} + + +def get_smac_map_registry(): + return map_param_registry + + +for name, map_params in map_param_registry.items(): + globals()[name] = type( + name, (SMACMap,), dict(filename=map_params["map_name"]) + ) diff --git a/src/envs/smac_v2/official/render.py b/src/envs/smac_v2/official/render.py new file mode 100644 index 0000000..8fb1216 --- /dev/null +++ b/src/envs/smac_v2/official/render.py @@ -0,0 +1,347 @@ +import numpy as np +import re +import subprocess +import platform +from absl import logging +import math +import time +import collections +import os +import pygame +import queue + +from pysc2.lib import colors +from pysc2.lib import point +from pysc2.lib.renderer_human import _Surface +from pysc2.lib import transform +from pysc2.lib import features + + +def clamp(n, smallest, largest): + return max(smallest, min(n, largest)) + + +def _get_desktop_size(): + """Get the desktop size.""" + if platform.system() == "Linux": + try: + xrandr_query = subprocess.check_output(["xrandr", "--query"]) + sizes = re.findall( + r"\bconnected primary (\d+)x(\d+)", str(xrandr_query) + ) + if sizes[0]: + return point.Point(int(sizes[0][0]), int(sizes[0][1])) + except ValueError: + logging.error("Failed to get the resolution from xrandr.") + + # Most general, but doesn't understand multiple monitors. + display_info = pygame.display.Info() + return point.Point(display_info.current_w, display_info.current_h) + + +class StarCraft2Renderer: + def __init__(self, env, mode): + os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "hide" + + self.env = env + self.mode = mode + self.obs = None + self._window_scale = 0.75 + self.game_info = game_info = self.env._controller.game_info() + self.static_data = self.env._controller.data() + + self._obs_queue = queue.Queue() + self._game_times = collections.deque( + maxlen=100 + ) # Avg FPS over 100 frames. # pytype: disable=wrong-keyword-args + self._render_times = collections.deque( + maxlen=100 + ) # pytype: disable=wrong-keyword-args + self._last_time = time.time() + self._last_game_loop = 0 + self._name_lengths = {} + + self._map_size = point.Point.build(game_info.start_raw.map_size) + self._playable = point.Rect( + point.Point.build(game_info.start_raw.playable_area.p0), + point.Point.build(game_info.start_raw.playable_area.p1), + ) + + window_size_px = point.Point( + self.env.window_size[0], self.env.window_size[1] + ) + window_size_px = self._map_size.scale_max_size( + window_size_px * self._window_scale + ).ceil() + self._scale = window_size_px.y // 32 + + self.display = pygame.Surface(window_size_px) + + if mode == "human": + self.display = pygame.display.set_mode(window_size_px, 0, 32) + pygame.display.init() + + pygame.display.set_caption("Starcraft Viewer") + pygame.font.init() + self._world_to_world_tl = transform.Linear( + point.Point(1, -1), point.Point(0, self._map_size.y) + ) + self._world_tl_to_screen = transform.Linear(scale=window_size_px / 32) + self.screen_transform = transform.Chain( + self._world_to_world_tl, self._world_tl_to_screen + ) + + surf_loc = point.Rect(point.origin, window_size_px) + sub_surf = self.display.subsurface( + pygame.Rect(surf_loc.tl, surf_loc.size) + ) + self._surf = _Surface( + sub_surf, + None, + surf_loc, + self.screen_transform, + None, + self.draw_screen, + ) + + self._font_small = pygame.font.Font(None, int(self._scale * 0.5)) + self._font_large = pygame.font.Font(None, self._scale) + + def close(self): + pygame.display.quit() + pygame.quit() + + def _get_units(self): + for u in sorted( + self.obs.observation.raw_data.units, + key=lambda u: (u.pos.z, u.owner != 16, -u.radius, u.tag), + ): + yield u, point.Point.build(u.pos) + + def get_unit_name(self, surf, name, radius): + """Get a length limited unit name for drawing units.""" + key = (name, radius) + if key not in self._name_lengths: + max_len = surf.world_to_surf.fwd_dist(radius * 1.6) + for i in range(len(name)): + if self._font_small.size(name[: i + 1])[0] > max_len: + self._name_lengths[key] = name[:i] + break + else: + self._name_lengths[key] = name + return self._name_lengths[key] + + def render(self, mode): + self.obs = self.env._obs + self.score = self.env.reward + self.step = self.env._episode_steps + + now = time.time() + self._game_times.append( + ( + now - self._last_time, + max( + 1, + self.obs.observation.game_loop + - self.obs.observation.game_loop, + ), + ) + ) + + if mode == "human": + pygame.event.pump() + + self._surf.draw(self._surf) + + observation = np.array(pygame.surfarray.pixels3d(self.display)) + + if mode == "human": + pygame.display.flip() + + self._last_time = now + self._last_game_loop = self.obs.observation.game_loop + # self._obs_queue.put(self.obs) + return ( + np.transpose(observation, axes=(1, 0, 2)) + if mode == "rgb_array" + else None + ) + + def draw_base_map(self, surf): + """Draw the base map.""" + hmap_feature = features.SCREEN_FEATURES.height_map + hmap = self.env.terrain_height * 255 + hmap = hmap.astype(np.uint8) + if ( + self.env.map_name == "corridor" + or self.env.map_name == "so_many_baneling" + or self.env.map_name == "2s_vs_1sc" + ): + hmap = np.flip(hmap) + else: + hmap = np.rot90(hmap, axes=(1, 0)) + if not hmap.any(): + hmap = hmap + 100 # pylint: disable=g-no-augmented-assignment + hmap_color = hmap_feature.color(hmap) + out = hmap_color * 0.6 + + surf.blit_np_array(out) + + def draw_units(self, surf): + """Draw the units.""" + unit_dict = None # Cache the units {tag: unit_proto} for orders. + tau = 2 * math.pi + for u, p in self._get_units(): + fraction_damage = clamp( + (u.health_max - u.health) / (u.health_max or 1), 0, 1 + ) + surf.draw_circle( + colors.PLAYER_ABSOLUTE_PALETTE[u.owner], p, u.radius + ) + + if fraction_damage > 0: + surf.draw_circle( + colors.PLAYER_ABSOLUTE_PALETTE[u.owner] // 2, + p, + u.radius * fraction_damage, + ) + surf.draw_circle(colors.black, p, u.radius, thickness=1) + + if self.static_data.unit_stats[u.unit_type].movement_speed > 0: + surf.draw_arc( + colors.white, + p, + u.radius, + u.facing - 0.1, + u.facing + 0.1, + thickness=1, + ) + + def draw_arc_ratio( + color, world_loc, radius, start, end, thickness=1 + ): + surf.draw_arc( + color, world_loc, radius, start * tau, end * tau, thickness + ) + + if u.shield and u.shield_max: + draw_arc_ratio( + colors.blue, p, u.radius - 0.05, 0, u.shield / u.shield_max + ) + + if u.energy and u.energy_max: + draw_arc_ratio( + colors.purple * 0.9, + p, + u.radius - 0.1, + 0, + u.energy / u.energy_max, + ) + elif u.orders and 0 < u.orders[0].progress < 1: + draw_arc_ratio( + colors.cyan, p, u.radius - 0.15, 0, u.orders[0].progress + ) + if u.buff_duration_remain and u.buff_duration_max: + draw_arc_ratio( + colors.white, + p, + u.radius - 0.2, + 0, + u.buff_duration_remain / u.buff_duration_max, + ) + if u.attack_upgrade_level: + draw_arc_ratio( + self.upgrade_colors[u.attack_upgrade_level], + p, + u.radius - 0.25, + 0.18, + 0.22, + thickness=3, + ) + if u.armor_upgrade_level: + draw_arc_ratio( + self.upgrade_colors[u.armor_upgrade_level], + p, + u.radius - 0.25, + 0.23, + 0.27, + thickness=3, + ) + if u.shield_upgrade_level: + draw_arc_ratio( + self.upgrade_colors[u.shield_upgrade_level], + p, + u.radius - 0.25, + 0.28, + 0.32, + thickness=3, + ) + + def write_small(loc, s): + surf.write_world(self._font_small, colors.white, loc, str(s)) + + name = self.get_unit_name( + surf, + self.static_data.units.get(u.unit_type, ""), + u.radius, + ) + + if name: + write_small(p, name) + + start_point = p + for o in u.orders: + target_point = None + if o.HasField("target_unit_tag"): + if unit_dict is None: + unit_dict = { + t.tag: t + for t in self.obs.observation.raw_data.units + } + target_unit = unit_dict.get(o.target_unit_tag) + if target_unit: + target_point = point.Point.build(target_unit.pos) + if target_point: + surf.draw_line(colors.cyan, start_point, target_point) + start_point = target_point + else: + break + + def draw_overlay(self, surf): + """Draw the overlay describing resources.""" + obs = self.obs.observation + times, steps = zip(*self._game_times) + sec = obs.game_loop // 22.4 + surf.write_screen( + self._font_large, + colors.green, + (-0.2, 0.2), + "Score: %s, Step: %s, %.1f/s, Time: %d:%02d" + % ( + self.score, + self.step, + sum(steps) / (sum(times) or 1), + sec // 60, + sec % 60, + ), + align="right", + ) + surf.write_screen( + self._font_large, + colors.green * 0.8, + (-0.2, 1.2), + "APM: %d, EPM: %d, FPS: O:%.1f, R:%.1f" + % ( + obs.score.score_details.current_apm, + obs.score.score_details.current_effective_apm, + len(times) / (sum(times) or 1), + len(self._render_times) / (sum(self._render_times) or 1), + ), + align="right", + ) + + def draw_screen(self, surf): + """Draw the screen area.""" + self.draw_base_map(surf) + self.draw_units(surf) + self.draw_overlay(surf) diff --git a/src/envs/smac_v2/official/sc2_official.py b/src/envs/smac_v2/official/sc2_official.py new file mode 100644 index 0000000..fdeae4f --- /dev/null +++ b/src/envs/smac_v2/official/sc2_official.py @@ -0,0 +1,2485 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from smac.env.multiagentenv import MultiAgentEnv + +from smac.env.starcraft2.maps import get_map_params + + +import atexit +from warnings import warn +from operator import attrgetter +from copy import deepcopy +import numpy as np +import enum +import math +from absl import logging +from pysc2.lib.units import Neutral, Protoss, Terran, Zerg + +from pysc2 import maps +from pysc2 import run_configs +from pysc2.lib import protocol + +from s2clientprotocol import common_pb2 as sc_common +from s2clientprotocol import sc2api_pb2 as sc_pb +from s2clientprotocol import raw_pb2 as r_pb +from s2clientprotocol import debug_pb2 as d_pb + +races = { + "R": sc_common.Random, + "P": sc_common.Protoss, + "T": sc_common.Terran, + "Z": sc_common.Zerg, +} + +difficulties = { + "1": sc_pb.VeryEasy, + "2": sc_pb.Easy, + "3": sc_pb.Medium, + "4": sc_pb.MediumHard, + "5": sc_pb.Hard, + "6": sc_pb.Harder, + "7": sc_pb.VeryHard, + "8": sc_pb.CheatVision, + "9": sc_pb.CheatMoney, + "A": sc_pb.CheatInsane, +} + +actions = { + "move": 16, # target: PointOrUnit + "attack": 23, # target: PointOrUnit + "stop": 4, # target: None + "heal": 386, # Unit +} + + +class Direction(enum.IntEnum): + NORTH = 0 + SOUTH = 1 + EAST = 2 + WEST = 3 + + +EPS = 1e-7 + + +class StarCraft2Env(MultiAgentEnv): + """The StarCraft II environment for decentralised multi-agent + micromanagement scenarios. + """ + + def __init__( + self, + map_name="8m", + step_mul=8, + move_amount=2, + difficulty="7", + game_version=None, + seed=None, + continuing_episode=False, + obs_all_health=True, + obs_own_health=True, + obs_last_action=False, + obs_pathing_grid=False, + obs_terrain_height=False, + obs_instead_of_state=False, + obs_timestep_number=False, + obs_own_pos=False, + obs_starcraft=True, + conic_fov=False, + num_fov_actions=12, + state_last_action=True, + state_timestep_number=False, + reward_sparse=False, + reward_only_positive=True, + reward_death_value=10, + reward_win=200, + reward_defeat=0, + reward_negative_scale=0.5, + reward_scale=True, + reward_scale_rate=20, + kill_unit_step_mul=2, + fully_observable=False, + capability_config={}, + replay_dir="", + replay_prefix="", + window_size_x=1920, + window_size_y=1200, + heuristic_ai=False, + heuristic_rest=False, + debug=False, + ): + """ + Create a StarCraftC2Env environment. + + Parameters + ---------- + map_name : str, optional + The name of the SC2 map to play (default is "8m"). The full list + can be found by running bin/map_list. + step_mul : int, optional + How many game steps per agent step (default is 8). None + indicates to use the default map step_mul. + move_amount : float, optional + How far away units are ordered to move per step (default is 2). + difficulty : str, optional + The difficulty of built-in computer AI bot (default is "7"). + game_version : str, optional + StarCraft II game version (default is None). None indicates the + latest version. + seed : int, optional + Random seed used during game initialisation. This allows to + continuing_episode : bool, optional + Whether to consider episodes continuing or finished after time + limit is reached (default is False). + obs_all_health : bool, optional + Agents receive the health of all units (in the sight range) as part + of observations (default is True). + obs_own_health : bool, optional + Agents receive their own health as a part of observations (default + is False). This flag is ignored when obs_all_health == True. + obs_last_action : bool, optional + Agents receive the last actions of all units (in the sight range) + as part of observations (default is False). + obs_pathing_grid : bool, optional + Whether observations include pathing values surrounding the agent + (default is False). + obs_terrain_height : bool, optional + Whether observations include terrain height values surrounding the + agent (default is False). + obs_instead_of_state : bool, optional + Use combination of all agents' observations as the global state + (default is False). + obs_timestep_number : bool, optional + Whether observations include the current timestep of the episode + (default is False). + state_last_action : bool, optional + Include the last actions of all agents as part of the global state + (default is True). + state_timestep_number : bool, optional + Whether the state include the current timestep of the episode + (default is False). + reward_sparse : bool, optional + Receive 1/-1 reward for winning/loosing an episode (default is + False). Whe rest of reward parameters are ignored if True. + reward_only_positive : bool, optional + Reward is always positive (default is True). + reward_death_value : float, optional + The amount of reward received for killing an enemy unit (default + is 10). This is also the negative penalty for having an allied unit + killed if reward_only_positive == False. + reward_win : float, optional + The reward for winning in an episode (default is 200). + reward_defeat : float, optional + The reward for loosing in an episode (default is 0). This value + should be nonpositive. + reward_negative_scale : float, optional + Scaling factor for negative rewards (default is 0.5). This + parameter is ignored when reward_only_positive == True. + reward_scale : bool, optional + Whether or not to scale the reward (default is True). + reward_scale_rate : float, optional + Reward scale rate (default is 20). When reward_scale == True, the + reward received by the agents is divided by (max_reward / + reward_scale_rate), where max_reward is the maximum possible + reward per episode without considering the shield regeneration + of Protoss units. + replay_dir : str, optional + The directory to save replays (default is None). If None, the + replay will be saved in Replays directory where StarCraft II is + installed. + replay_prefix : str, optional + The prefix of the replay to be saved (default is None). If None, + the name of the map will be used. + window_size_x : int, optional + The length of StarCraft II window size (default is 1920). + window_size_y: int, optional + The height of StarCraft II window size (default is 1200). + heuristic_ai: bool, optional + Whether or not to use a non-learning heuristic AI (default False). + heuristic_rest: bool, optional + At any moment, restrict the actions of the heuristic AI to be + chosen from actions available to RL agents (default is False). + Ignored if heuristic_ai == False. + debug: bool, optional + Log messages about observations, state, actions and rewards for + debugging purposes (default is False). + """ + # Map arguments + self.map_name = map_name + map_params = get_map_params(self.map_name) + self.map_params = map_params + self.episode_limit = map_params["limit"] + self._move_amount = move_amount + self._step_mul = step_mul + self._kill_unit_step_mul = kill_unit_step_mul + self.difficulty = difficulty + + # Observations and state + self.obs_own_health = obs_own_health + self.obs_all_health = obs_all_health + self.obs_instead_of_state = obs_instead_of_state + self.obs_last_action = obs_last_action + self.obs_pathing_grid = obs_pathing_grid + self.obs_terrain_height = obs_terrain_height + self.obs_timestep_number = obs_timestep_number + self.obs_starcraft = obs_starcraft + self.state_last_action = state_last_action + self.state_timestep_number = state_timestep_number + if self.obs_all_health: + self.obs_own_health = True + self.n_obs_pathing = 8 + self.n_obs_height = 9 + + # Rewards args + self.reward_sparse = reward_sparse + self.reward_only_positive = reward_only_positive + self.reward_negative_scale = reward_negative_scale + self.reward_death_value = reward_death_value + self.reward_win = reward_win + self.reward_defeat = reward_defeat + self.reward_scale = reward_scale + self.reward_scale_rate = reward_scale_rate + + # Meta MARL + self.capability_config = capability_config + self.fully_observable = fully_observable + self.stochastic_attack = "attack" in self.capability_config + self.stochastic_health = "health" in self.capability_config + self.replace_teammates = "team_gen" in self.capability_config + self.obs_own_pos = obs_own_pos + self.mask_enemies = "enemy_mask" in self.capability_config + if self.stochastic_attack: + self.zero_pad_stochastic_attack = not self.capability_config[ + "attack" + ]["observe"] + self.observe_attack_probs = self.capability_config["attack"][ + "observe" + ] + if self.stochastic_health: + self.zero_pad_health = not self.capability_config["health"][ + "observe" + ] + self.observe_teammate_health = self.capability_config["health"][ + "observe" + ] + if self.replace_teammates: + self.zero_pad_unit_types = not self.capability_config["team_gen"][ + "observe" + ] + self.observe_teammate_types = self.capability_config["team_gen"][ + "observe" + ] + self.n_agents = ( + map_params["n_agents"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.n_enemies = ( + map_params["n_enemies"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.random_start = "start_positions" in self.capability_config + self.conic_fov = conic_fov + self.n_fov_actions = num_fov_actions if self.conic_fov else 0 + self.conic_fov_angle = ( + (2 * np.pi) / self.n_fov_actions if self.conic_fov else 0 + ) + # Other + self.game_version = game_version + self.continuing_episode = continuing_episode + self._seed = seed + self.heuristic_ai = heuristic_ai + self.heuristic_rest = heuristic_rest + self.debug = debug + self.window_size = (window_size_x, window_size_y) + self.replay_dir = replay_dir + self.replay_prefix = replay_prefix + + # Actions + self.n_actions_move = 4 + + self.n_actions_no_attack = self.n_actions_move + self.n_fov_actions + 2 + self.n_actions = self.n_actions_no_attack + self.n_enemies + + # Map info + self._agent_race = map_params["a_race"] + self._bot_race = map_params["b_race"] + self.shield_bits_ally = 1 if self._agent_race == "P" else 0 + self.shield_bits_enemy = 1 if self._bot_race == "P" else 0 + # NOTE: The map_type, which is used to initialise the unit + # type ids, the unit_type_bits and the races, are still properties of the + # map. This means even the 10gen_{race} maps are limited to the + # unit types statically defined in the unit type id assignment. + # Lifting this restriction shouldn't be too much work, I've just + # not done it. + self.unit_type_bits = map_params["unit_type_bits"] + self.map_type = map_params["map_type"] + self._unit_types = None + + self.max_reward = ( + self.n_enemies * self.reward_death_value + self.reward_win + ) + + # create lists containing the names of attributes returned in states + self.ally_state_attr_names = [ + "health", + "energy/cooldown", + "rel_x", + "rel_y", + ] + self.enemy_state_attr_names = ["health", "rel_x", "rel_y"] + + if self.shield_bits_ally > 0: + self.ally_state_attr_names += ["shield"] + if self.shield_bits_enemy > 0: + self.enemy_state_attr_names += ["shield"] + if self.conic_fov: + self.ally_state_attr_names += ["fov_x", "fov_y"] + + self.capability_attr_names = [] + if "attack" in self.capability_config: + self.capability_attr_names += ["attack_probability"] + if "health" in self.capability_config: + self.capability_attr_names += ["total_health"] + if self.unit_type_bits > 0: + bit_attr_names = [ + "type_{}".format(bit) for bit in range(self.unit_type_bits) + ] + self.capability_attr_names += bit_attr_names + self.enemy_state_attr_names += bit_attr_names + + self.agents = {} + self.enemies = {} + self.unit_name_to_id_map = {} + self.id_to_unit_name_map = {} + self._episode_count = 0 + self._episode_steps = 0 + self._total_steps = 0 + self._obs = None + self.battles_won = 0 + self.battles_game = 0 + self.timeouts = 0 + self.force_restarts = 0 + self.last_stats = None + self.agent_attack_probabilities = np.zeros(self.n_agents) + self.agent_health_levels = np.zeros(self.n_agents) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.canonical_fov_directions = np.array( + [ + ( + np.cos(2 * np.pi * (i / self.n_fov_actions)), + np.sin(2 * np.pi * (i / self.n_fov_actions)), + ) + for i in range(self.n_fov_actions) + ] + ) + self.new_unit_positions = np.zeros((self.n_agents, 2)) + self.previous_ally_units = None + self.previous_enemy_units = None + self.last_action = np.zeros((self.n_agents, self.n_actions)) + self.init_positions = np.zeros((self.n_agents, 2)) + self._min_unit_type = 0 + self.marine_id = self.marauder_id = self.medivac_id = 0 + self.hydralisk_id = self.zergling_id = self.baneling_id = 0 + self.stalker_id = self.colossus_id = self.zealot_id = 0 + self.max_distance_x = 0 + self.max_distance_y = 0 + self.map_x = 0 + self.map_y = 0 + self.reward = 0 + self.renderer = None + self.terrain_height = None + self.pathing_grid = None + self.state_feature_names = self.build_state_feature_names() + self.obs_feature_names = self.build_obs_feature_names() + self._run_config = None + self._sc2_proc = None + self._controller = None + # Try to avoid leaking SC2 processes on shutdown + atexit.register(lambda: self.close()) + + def _only_one_meta_marl_flag_on(self): + """Function that checks that either all the meta marl flags are off, + or at most one has been enabled.""" + if self.stochastic_attack: + return not self.stochastic_health and not self.replace_teammates + else: + return not self.replace_teammates or not self.stochastic_health + + def _launch(self): + """Launch the StarCraft II game.""" + self._run_config = run_configs.get(version=self.game_version) + self.version = self._run_config.version + _map = maps.get(self.map_name) + + # Setting up the interface + interface_options = sc_pb.InterfaceOptions(raw=True, score=False) + self._sc2_proc = self._run_config.start( + window_size=self.window_size, want_rgb=False + ) + self._controller = self._sc2_proc.controller + + # Request to create the game + create = sc_pb.RequestCreateGame( + local_map=sc_pb.LocalMap( + map_path=_map.path, + map_data=self._run_config.map_data(_map.path), + ), + realtime=False, + random_seed=self._seed, + ) + create.player_setup.add(type=sc_pb.Participant) + create.player_setup.add( + type=sc_pb.Computer, + race=races[self._bot_race], + difficulty=difficulties[self.difficulty], + ) + self._controller.create_game(create) + + join = sc_pb.RequestJoinGame( + race=races[self._agent_race], options=interface_options + ) + self._controller.join_game(join) + + game_info = self._controller.game_info() + map_info = game_info.start_raw + self.map_play_area_min = map_info.playable_area.p0 + self.map_play_area_max = map_info.playable_area.p1 + self.max_distance_x = ( + self.map_play_area_max.x - self.map_play_area_min.x + ) + self.max_distance_y = ( + self.map_play_area_max.y - self.map_play_area_min.y + ) + self.map_x = map_info.map_size.x + self.map_y = map_info.map_size.y + + if map_info.pathing_grid.bits_per_pixel == 1: + vals = np.array(list(map_info.pathing_grid.data)).reshape( + self.map_x, int(self.map_y / 8) + ) + self.pathing_grid = np.transpose( + np.array( + [ + [(b >> i) & 1 for b in row for i in range(7, -1, -1)] + for row in vals + ], + dtype=np.bool, + ) + ) + else: + self.pathing_grid = np.invert( + np.flip( + np.transpose( + np.array( + list(map_info.pathing_grid.data), dtype=np.bool + ).reshape(self.map_x, self.map_y) + ), + axis=1, + ) + ) + + self.terrain_height = ( + np.flip( + np.transpose( + np.array(list(map_info.terrain_height.data)).reshape( + self.map_x, self.map_y + ) + ), + 1, + ) + / 255 + ) + + def reset(self, episode_config={}): + """Reset the environment. Required after each full episode. + Returns initial observations and states. + """ + self._episode_steps = 0 + self.episode_config = episode_config + if self._episode_count == 0: + # Launch StarCraft II + self._launch() + else: + self._restart() + + # Information kept for counting the reward + self.agent_attack_probabilities = episode_config.get("attack", {}).get( + "item", None + ) + self.agent_health_levels = episode_config.get("health", {}).get( + "item", None + ) + self.enemy_mask = episode_config.get("enemy_mask", {}).get( + "item", None + ) + self.ally_start_positions = episode_config.get( + "ally_start_positions", {} + ).get("item", None) + self.enemy_start_positions = episode_config.get( + "enemy_start_positions", {} + ).get("item", None) + self.mask_enemies = self.enemy_mask is not None + team = episode_config.get("team_gen", {}).get("item", None) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.previous_ally_units = None + self.previous_enemy_units = None + self.win_counted = False + self.defeat_counted = False + if self.debug: + logging.debug( + f"Attack Probabilities: {self.agent_attack_probabilities}" + ) + logging.debug(f"Health Levels: {self.agent_health_levels}") + self.last_action = np.zeros((self.n_agents, self.n_actions)) + + if self.heuristic_ai: + self.heuristic_targets = [None] * self.n_agents + + try: + self._obs = self._controller.observe() + self.init_units(team, episode_config=episode_config) + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + if self.debug: + logging.debug( + "Started Episode {}".format(self._episode_count).center( + 60, "*" + ) + ) + return self.get_obs(), self.get_state() + + def _restart(self): + """Restart the environment by killing all units on the map. + There is a trigger in the SC2Map file, which restarts the + episode when there are no units left. + """ + try: + self._kill_all_units() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + def full_restart(self): + """Full restart. Closes the SC2 process and launches a new one.""" + self._sc2_proc.close() + self._launch() + self.force_restarts += 1 + + def _kill_units_below_health_level(self): + units_to_kill = [] + for al_id, al_unit in self.agents.items(): + if ( + al_unit.health / al_unit.health_max + < self.agent_health_levels[al_id] + ) and not self.death_tracker_ally[al_id]: + units_to_kill.append(al_unit.tag) + self._kill_units(units_to_kill) + + def step(self, actions): + """A single environment step. Returns reward, terminated, info.""" + actions_int = [int(a) for a in actions] + + self.last_action = np.eye(self.n_actions)[np.array(actions_int)] + + # Collect individual actions + sc_actions = [] + if self.debug: + logging.debug("Actions".center(60, "-")) + + for a_id, action in enumerate(actions_int): + if not self.heuristic_ai: + sc_action = self.get_agent_action(a_id, action) + else: + sc_action, action_num = self.get_agent_action_heuristic( + a_id, action + ) + actions[a_id] = action_num + if sc_action: + sc_actions.append(sc_action) + # Send action request + req_actions = sc_pb.RequestAction(actions=sc_actions) + + try: + + if self.conic_fov: + self.render_fovs() + self._controller.actions(req_actions) + # Make step in SC2, i.e. apply actions + if not self.stochastic_health: + self._controller.step(self._step_mul) + else: + self._controller.step( + self._step_mul - self._kill_unit_step_mul + ) + self._kill_units_below_health_level() + self._controller.step(self._kill_unit_step_mul) + # Observe here so that we know if the episode is over. + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + return 0, True, {} + + self._total_steps += 1 + self._episode_steps += 1 + + # Update units + game_end_code = self.update_units() + + terminated = False + reward = self.reward_battle() + info = {"battle_won": False} + + # count units that are still alive + dead_allies, dead_enemies = 0, 0 + for _al_id, al_unit in self.agents.items(): + if al_unit.health == 0: + dead_allies += 1 + for _e_id, e_unit in self.enemies.items(): + if e_unit.health == 0: + dead_enemies += 1 + + info["dead_allies"] = dead_allies + info["dead_enemies"] = dead_enemies + + if game_end_code is not None: + # Battle is over + terminated = True + self.battles_game += 1 + if game_end_code == 1 and not self.win_counted: + self.battles_won += 1 + self.win_counted = True + info["battle_won"] = True + if not self.reward_sparse: + reward += self.reward_win + else: + reward = 1 + elif game_end_code == -1 and not self.defeat_counted: + self.defeat_counted = True + if not self.reward_sparse: + reward += self.reward_defeat + else: + reward = -1 + + elif self._episode_steps >= self.episode_limit: + # Episode limit reached + terminated = True + if self.continuing_episode: + info["episode_limit"] = True + self.battles_game += 1 + self.timeouts += 1 + + if self.debug: + logging.debug("Reward = {}".format(reward).center(60, "-")) + + if terminated: + self._episode_count += 1 + + if self.reward_scale: + reward /= self.max_reward / self.reward_scale_rate + + self.reward = reward + + return reward, terminated, info + + def get_agent_action(self, a_id, action): + """Construct the action for agent a_id.""" + avail_actions = self.get_avail_agent_actions(a_id) + assert ( + avail_actions[action] == 1 + ), "Agent {} cannot perform action {}".format(a_id, action) + + unit = self.get_unit_by_id(a_id) + tag = unit.tag + x = unit.pos.x + y = unit.pos.y + + if action == 0: + # no-op (valid only when dead) + assert unit.health == 0, "No-op only available for dead agents." + if self.debug: + logging.debug("Agent {}: Dead".format(a_id)) + return None + elif action == 1: + # stop + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["stop"], + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Stop".format(a_id)) + + elif action == 2: + # move north + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y + self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y + self._move_amount] + ) + if self.debug: + logging.debug("Agent {}: Move North".format(a_id)) + + elif action == 3: + # move south + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y - self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y - self._move_amount] + ) + if self.debug: + logging.debug("Agent {}: Move South".format(a_id)) + + elif action == 4: + # move east + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x + self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x + self._move_amount, y] + ) + + if self.debug: + logging.debug("Agent {}: Move East".format(a_id)) + + elif action == 5: + # move west + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x - self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x - self._move_amount, y] + ) + if self.debug: + logging.debug("Agent {}: Move West".format(a_id)) + elif self.conic_fov and action in range(6, 6 + self.n_fov_actions): + self.fov_directions[a_id] = self.canonical_fov_directions[ + action - 6 + ] + cmd = None + else: + # attack/heal units that are in range + target_id = action - self.n_actions_no_attack + if ( + self.map_type in ["MMM", "terran_gen"] + and unit.unit_type == self.medivac_id + ): + target_unit = self.agents[target_id] + action_name = "heal" + else: + target_unit = self.enemies[target_id] + action_name = "attack" + + if self.stochastic_attack: + p = np.random.default_rng().uniform() + if p > self.agent_attack_probabilities[a_id]: + if self.debug: + logging.debug( + f"Agent {a_id} {action_name}s {target_id}, but fails" + ) + return None + action_id = actions[action_name] + target_tag = target_unit.tag + + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + if self.debug: + logging.debug( + "Agent {} {}s unit # {}".format( + a_id, action_name, target_id + ) + ) + if cmd: + sc_action = sc_pb.Action( + action_raw=r_pb.ActionRaw(unit_command=cmd) + ) + return sc_action + return None + + def get_agent_action_heuristic(self, a_id, action): + unit = self.get_unit_by_id(a_id) + tag = unit.tag + + target = self.heuristic_targets[a_id] + if unit.unit_type == self.medivac_id: + if ( + target is None + or self.agents[target].health == 0 + or self.agents[target].health == self.agents[target].health_max + ): + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for al_id, al_unit in self.agents.items(): + if al_unit.unit_type == self.medivac_id: + continue + if ( + al_unit.health != 0 + and al_unit.health != al_unit.health_max + ): + dist = self.distance( + unit.pos.x, + unit.pos.y, + al_unit.pos.x, + al_unit.pos.y, + ) + if dist < min_dist: + min_dist = dist + min_id = al_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["heal"] + target_tag = self.agents[self.heuristic_targets[a_id]].tag + else: + if target is None or self.enemies[target].health == 0: + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for e_id, e_unit in self.enemies.items(): + if ( + unit.unit_type == self.marauder_id + and e_unit.unit_type == self.medivac_id + ): + continue + if e_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, e_unit.pos.x, e_unit.pos.y + ) + if dist < min_dist: + min_dist = dist + min_id = e_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["attack"] + target_tag = self.enemies[self.heuristic_targets[a_id]].tag + + action_num = self.heuristic_targets[a_id] + self.n_actions_no_attack + + # Check if the action is available + if ( + self.heuristic_rest + and self.get_avail_agent_actions(a_id)[action_num] == 0 + ): + + # Move towards the target rather than attacking/healing + if unit.unit_type == self.medivac_id: + target_unit = self.agents[self.heuristic_targets[a_id]] + else: + target_unit = self.enemies[self.heuristic_targets[a_id]] + + delta_x = target_unit.pos.x - unit.pos.x + delta_y = target_unit.pos.y - unit.pos.y + + if abs(delta_x) > abs(delta_y): # east or west + if delta_x > 0: # east + target_pos = sc_common.Point2D( + x=unit.pos.x + self._move_amount, y=unit.pos.y + ) + action_num = 4 + else: # west + target_pos = sc_common.Point2D( + x=unit.pos.x - self._move_amount, y=unit.pos.y + ) + action_num = 5 + else: # north or south + if delta_y > 0: # north + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y + self._move_amount + ) + action_num = 2 + else: # south + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y - self._move_amount + ) + action_num = 3 + + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=target_pos, + unit_tags=[tag], + queue_command=False, + ) + else: + # Attack/heal the target + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd)) + return sc_action, action_num + + def reward_battle(self): + """Reward function when self.reward_spare==False. + Returns accumulative hit/shield point damage dealt to the enemy + + reward_death_value per enemy unit killed, and, in case + self.reward_only_positive == False, - (damage dealt to ally units + + reward_death_value per ally unit killed) * self.reward_negative_scale + """ + assert ( + not self.stochastic_health or self.reward_only_positive + ), "Different Health Levels are currently only compatible with positive rewards" + if self.reward_sparse: + return 0 + + reward = 0 + delta_deaths = 0 + delta_ally = 0 + delta_enemy = 0 + + neg_scale = self.reward_negative_scale + + # update deaths + for al_id, al_unit in self.agents.items(): + if not self.death_tracker_ally[al_id]: + # did not die so far + prev_health = ( + self.previous_ally_units[al_id].health + + self.previous_ally_units[al_id].shield + ) + if al_unit.health == 0: + # just died + self.death_tracker_ally[al_id] = 1 + if not self.reward_only_positive: + delta_deaths -= self.reward_death_value * neg_scale + delta_ally += prev_health * neg_scale + else: + # still alive + delta_ally += neg_scale * ( + prev_health - al_unit.health - al_unit.shield + ) + + for e_id, e_unit in self.enemies.items(): + if not self.death_tracker_enemy[e_id]: + prev_health = ( + self.previous_enemy_units[e_id].health + + self.previous_enemy_units[e_id].shield + ) + if e_unit.health == 0: + self.death_tracker_enemy[e_id] = 1 + delta_deaths += self.reward_death_value + delta_enemy += prev_health + else: + delta_enemy += prev_health - e_unit.health - e_unit.shield + + if self.reward_only_positive: + reward = max(delta_enemy + delta_deaths, 0) # shield regeneration + else: + reward = delta_enemy + delta_deaths - delta_ally + + return reward + + def get_total_actions(self): + """Returns the total number of actions an agent could ever take.""" + return self.n_actions + + @staticmethod + def distance(x1, y1, x2, y2): + """Distance between two points.""" + return math.hypot(x2 - x1, y2 - y1) + + def unit_shoot_range(self, agent_id): + """Returns the shooting range for an agent.""" + return 6 + + def unit_sight_range(self, agent_id): + """Returns the sight range for an agent.""" + return 9 + + def unit_max_cooldown(self, unit): + """Returns the maximal cooldown for a unit.""" + switcher = { + self.marine_id: 15, + self.marauder_id: 25, + self.medivac_id: 200, # max energy + self.stalker_id: 35, + self.zealot_id: 22, + self.colossus_id: 24, + self.hydralisk_id: 10, + self.zergling_id: 11, + self.baneling_id: 1, + } + return switcher.get(unit.unit_type, 15) + + def save_replay(self): + """Save a replay.""" + prefix = self.replay_prefix or self.map_name + replay_dir = self.replay_dir or "" + replay_path = self._run_config.save_replay( + self._controller.save_replay(), + replay_dir=replay_dir, + prefix=prefix, + ) + logging.info("Replay saved at: %s" % replay_path) + + def unit_max_shield(self, unit): + """Returns maximal shield for a given unit.""" + if unit.unit_type == 74 or unit.unit_type == self.stalker_id: + return 80 # Protoss's Stalker + elif unit.unit_type == 73 or unit.unit_type == self.zealot_id: + return 50 # Protoss's Zealot + elif unit.unit_type == 4 or unit.unit_type == self.colossus_id: + return 150 # Protoss's Colossus + else: + raise Exception("Maximum shield not recognised") + + def build_state_feature_names(self): + """Return the state feature names.""" + if self.obs_instead_of_state: + raise NotImplementedError + + feature_names = [] + + # Ally features. + for al_id in range(self.n_agents): + feature_names.append(f"ally_health_{al_id}") + feature_names.append(f"ally_cooldown_{al_id}") + feature_names.append(f"ally_relative_x_{al_id}") + feature_names.append(f"ally_relative_y_{al_id}") + + if self.shield_bits_ally > 0: + feature_names.append(f"ally_shield_{al_id}") + + if self.stochastic_attack: + feature_names.append(f"ally_attack_prob_{al_id}") + + if self.stochastic_health: + feature_names.append(f"ally_health_level_{al_id}") + + if self.conic_fov: + feature_names.append(f"ally_fov_x_{al_id}") + feature_names.append(f"ally_fov_y_{al_id}") + + if self.unit_type_bits > 0: + for bit in range(self.unit_type_bits): + feature_names.append(f"ally_unit_type_{al_id}_bit_{bit}") + + # Enemy features. + for e_id in range(self.n_enemies): + feature_names.append(f"enemy_health_{e_id}") + feature_names.append(f"enemy_relative_x_{e_id}") + feature_names.append(f"enemy_relative_y_{e_id}") + + if self.shield_bits_enemy > 0: + feature_names.append(f"enemy_shield_{e_id}") + + if self.unit_type_bits > 0: + for bit in range(self.unit_type_bits): + feature_names.append(f"enemy_unit_type_{e_id}_bit_{bit}") + + if self.state_last_action: + for al_id in range(self.n_agents): + for action_idx in range(self.n_actions): + feature_names.append( + f"ally_last_action_{al_id}_action_{action_idx}" + ) + + if self.state_timestep_number: + feature_names.append("timestep") + + return feature_names + + def get_state_feature_names(self): + return self.state_feature_names + + def build_obs_feature_names(self): + """Return the observations feature names.""" + feature_names = [] + + # Movement features. + feature_names.extend( + [ + "move_action_north", + "move_action_south", + "move_action_east", + "move_action_west", + ] + ) + if self.obs_pathing_grid: + feature_names.extend( + [f"pathing_grid_{n}" for n in range(self.n_obs_pathing)] + ) + if self.obs_terrain_height: + feature_names.extend( + [f"terrain_height_{n}" for n in range(self.n_obs_height)] + ) + + # Enemy features. + for e_id in range(self.n_enemies): + feature_names.extend( + [ + f"enemy_shootable_{e_id}", + f"enemy_distance_{e_id}", + f"enemy_relative_x_{e_id}", + f"enemy_relative_y_{e_id}", + ] + ) + if self.obs_all_health: + feature_names.append(f"enemy_health_{e_id}") + if self.obs_all_health and self.shield_bits_enemy > 0: + feature_names.append(f"enemy_shield_{e_id}") + if self.unit_type_bits > 0: + feature_names.extend( + [ + f"enemy_unit_type_{e_id}_bit_{bit}" + for bit in range(self.unit_type_bits) + ] + ) + + # Ally features. + # From the perspective of agent 0. + al_ids = [al_id for al_id in range(self.n_agents) if al_id != 0] + for al_id in al_ids: + feature_names.extend( + [ + f"ally_visible_{al_id}", + f"ally_distance_{al_id}", + f"ally_relative_x_{al_id}", + f"ally_relative_y_{al_id}", + ] + ) + if self.obs_all_health: + feature_names.append(f"ally_health_{al_id}") + if self.shield_bits_ally > 0: + feature_names.append(f"ally_shield_{al_id}") + if self.stochastic_attack and ( + self.observe_attack_probs or self.zero_pad_stochastic_attack + ): + feature_names.append(f"ally_attack_prob_{al_id}") + if self.stochastic_health and ( + self.observe_teammate_health or self.zero_pad_health + ): + feature_names.append(f"ally_health_level_{al_id}") + if self.unit_type_bits > 0 and ( + (not self.replace_teammates or self.observe_teammate_types) + or self.zero_pad_unit_types + ): + feature_names.extend( + [ + f"ally_unit_type_{al_id}_bit_{bit}" + for bit in range(self.unit_type_bits) + ] + ) + if self.obs_last_action: + feature_names.extend( + [ + f"ally_last_action_{al_id}_action_{action}" + for action in range(self.n_actions) + ] + ) + + # Own features. + if self.obs_own_health: + feature_names.append("own_health") + if self.shield_bits_ally > 0: + feature_names.append("own_shield") + if self.stochastic_attack: + feature_names.append("own_attack_prob") + if self.stochastic_health: + feature_names.append("own_health_level") + if self.obs_own_pos: + feature_names.extend(["own_pos_x", "own_pos_y"]) + if self.conic_fov: + feature_names.extend(["own_fov_x", "own_fov_y"]) + if self.unit_type_bits > 0: + feature_names.extend( + [ + f"own_unit_type_bit_{bit}" + for bit in range(self.unit_type_bits) + ] + ) + if not self.obs_starcraft: + feature_names = [] + + if self.obs_timestep_number: + feature_names.append("timestep") + + return feature_names + + def get_obs_feature_names(self): + return self.obs_feature_names + + def can_move(self, unit, direction): + """Whether a unit can move in a given direction.""" + m = self._move_amount / 2 + + if direction == Direction.NORTH: + x, y = int(unit.pos.x), int(unit.pos.y + m) + elif direction == Direction.SOUTH: + x, y = int(unit.pos.x), int(unit.pos.y - m) + elif direction == Direction.EAST: + x, y = int(unit.pos.x + m), int(unit.pos.y) + else: + x, y = int(unit.pos.x - m), int(unit.pos.y) + + if self.check_bounds(x, y) and self.pathing_grid[x, y]: + return True + + return False + + def get_surrounding_points(self, unit, include_self=False): + """Returns the surrounding points of the unit in 8 directions.""" + x = int(unit.pos.x) + y = int(unit.pos.y) + + ma = self._move_amount + + points = [ + (x, y + 2 * ma), + (x, y - 2 * ma), + (x + 2 * ma, y), + (x - 2 * ma, y), + (x + ma, y + ma), + (x - ma, y - ma), + (x + ma, y - ma), + (x - ma, y + ma), + ] + + if include_self: + points.append((x, y)) + + return points + + def check_bounds(self, x, y): + """Whether a point is within the map bounds.""" + return 0 <= x < self.map_x and 0 <= y < self.map_y + + def get_surrounding_pathing(self, unit): + """Returns pathing values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=False) + vals = [ + self.pathing_grid[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def get_surrounding_height(self, unit): + """Returns height values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=True) + vals = [ + self.terrain_height[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def _compute_health(self, agent_id, unit): + """Each agent has a health bar with max health + `health_max` and current health `health`. We set a level + `health_level` between `0` and `1` where the agent dies if its + proportional health (`health / health_max`) is below that level. + This function rescales health to take into account this death level. + + In the proportional health scale we have something that looks like this: + + ------------------------------------------------------------- + 0 1 + ^ health_level ^ proportional_health + And so we compute + (proportional_health - health_level) / (1 - health_level) + """ + proportional_health = unit.health / unit.health_max + health_level = self.agent_health_levels[agent_id] + return (1.0 / (1 - health_level)) * ( + proportional_health - health_level + ) + + def render_fovs(self): + lines_to_render = [] + for agent_id in range(self.n_agents): + if not self.death_tracker_ally[agent_id]: + lines_to_render.extend(self.agent_cone(agent_id)) + debug_command = d_pb.DebugCommand( + draw=d_pb.DebugDraw(lines=lines_to_render) + ) + self._controller.debug(debug_command) + + def agent_cone(self, agent_id): + fov_direction = self.fov_directions[agent_id] + c, s = np.cos(self.conic_fov_angle / 2), np.sin( + self.conic_fov_angle / 2 + ) + sight_range = self.unit_sight_range(agent_id) + rot = np.array([[c, -s], [s, c]]) + neg_rot = np.array([[c, s], [-s, c]]) + start_pos = self.new_unit_positions[agent_id] + init_pos = sc_common.Point( + x=start_pos[0], + y=start_pos[1], + z=self.get_unit_by_id(agent_id).pos.z, + ) + upper_cone_end = start_pos + (rot @ fov_direction) * sight_range + lower_cone_end = start_pos + (neg_rot @ fov_direction) * sight_range + lines = [ + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=upper_cone_end[0], + y=upper_cone_end[1], + z=init_pos.z, + ), + ) + ), + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=lower_cone_end[0], + y=lower_cone_end[1], + z=init_pos.z, + ), + ) + ), + ] + return lines + + def is_position_in_cone(self, agent_id, pos, range="sight_range"): + ally_pos = self.get_unit_by_id(agent_id).pos + distance = self.distance(ally_pos.x, ally_pos.y, pos.x, pos.y) + # position is in this agent's cone if it is not outside the sight + # range and has the correct angle + if range == "sight_range": + unit_range = self.unit_sight_range(agent_id) + elif range == "shoot_range": + unit_range = self.unit_shoot_range(agent_id) + else: + raise Exception("Range argument not recognised") + if distance > unit_range: + return False + x_diff = pos.x - ally_pos.x + x_diff = max(x_diff, EPS) if x_diff > 0 else min(x_diff, -EPS) + obj_angle = np.arctan((pos.y - ally_pos.y) / x_diff) + x = self.fov_directions[agent_id][0] + x = max(x, EPS) if x_diff > 0 else min(x, -EPS) + fov_angle = np.arctan(self.fov_directions[agent_id][1] / x) + return np.abs(obj_angle - fov_angle) < self.conic_fov_angle / 2 + + def get_obs_agent(self, agent_id, fully_observable=False): + """Returns observation for agent_id. The observation is composed of: + + - agent movement features (where it can move to, height information + and pathing grid) + - enemy features (available_to_attack, health, relative_x, relative_y, + shield, unit_type) + - ally features (visible, distance, relative_x, relative_y, shield, + unit_type) + - agent unit features (health, shield, unit_type) + + All of this information is flattened and concatenated into a list, + in the aforementioned order. To know the sizes of each of the + features inside the final list of features, take a look at the + functions ``get_obs_move_feats_size()``, + ``get_obs_enemy_feats_size()``, ``get_obs_ally_feats_size()`` and + ``get_obs_own_feats_size()``. + + The size of the observation vector may vary, depending on the + environment configuration and type of units present in the map. + For instance, non-Protoss units will not have shields, movement + features may or may not include terrain height and pathing grid, + unit_type is not included if there is only one type of unit in the + map etc.). + + NOTE: Agents should have access only to their local observations + during decentralised execution. + + fully_observable: -- ignores sight range for a particular unit. + For Debugging purposes ONLY -- not a fair observation. + """ + unit = self.get_unit_by_id(agent_id) + + move_feats_dim = self.get_obs_move_feats_size() + enemy_feats_dim = self.get_obs_enemy_feats_size() + ally_feats_dim = self.get_obs_ally_feats_size() + own_feats_dim = self.get_obs_own_feats_size() + + move_feats = np.zeros(move_feats_dim, dtype=np.float32) + enemy_feats = np.zeros(enemy_feats_dim, dtype=np.float32) + ally_feats = np.zeros(ally_feats_dim, dtype=np.float32) + own_feats = np.zeros(own_feats_dim, dtype=np.float32) + + if ( + unit.health > 0 and self.obs_starcraft + ): # otherwise dead, return all zeros + x = unit.pos.x + y = unit.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Movement features. Do not need similar for looking + # around because this is always possible + avail_actions = self.get_avail_agent_actions(agent_id) + for m in range(self.n_actions_move): + move_feats[m] = avail_actions[m + 2] + + ind = self.n_actions_move + + if self.obs_pathing_grid: + move_feats[ + ind : ind + self.n_obs_pathing # noqa + ] = self.get_surrounding_pathing(unit) + ind += self.n_obs_pathing + + if self.obs_terrain_height: + move_feats[ind:] = self.get_surrounding_height(unit) + + # Enemy features + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + enemy_visible = ( + self.is_position_in_cone(agent_id, e_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (enemy_visible and e_unit.health > 0) or ( + e_unit.health > 0 and fully_observable + ): # visible and alive + # Sight range > shoot range + enemy_feats[e_id, 0] = avail_actions[ + self.n_actions_no_attack + e_id + ] # available + enemy_feats[e_id, 1] = dist / sight_range # distance + enemy_feats[e_id, 2] = ( + e_x - x + ) / sight_range # relative X + enemy_feats[e_id, 3] = ( + e_y - y + ) / sight_range # relative Y + show_enemy = ( + self.mask_enemies + and not self.enemy_mask[agent_id][e_id] + ) or not self.mask_enemies + ind = 4 + if self.obs_all_health and show_enemy: + enemy_feats[e_id, ind] = ( + e_unit.health / e_unit.health_max + ) # health + ind += 1 + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_feats[e_id, ind] = ( + e_unit.shield / max_shield + ) # shield + ind += 1 + + if self.unit_type_bits > 0 and show_enemy: + type_id = self.get_unit_type_id(e_unit, False) + enemy_feats[e_id, ind + type_id] = 1 # unit type + + # Ally features + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id != agent_id + ] + for i, al_id in enumerate(al_ids): + + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + ally_visible = ( + self.is_position_in_cone(agent_id, al_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (ally_visible and al_unit.health > 0) or ( + al_unit.health > 0 and fully_observable + ): # visible and alive + ally_feats[i, 0] = 1 # visible + ally_feats[i, 1] = dist / sight_range # distance + ally_feats[i, 2] = (al_x - x) / sight_range # relative X + ally_feats[i, 3] = (al_y - y) / sight_range # relative Y + + ind = 4 + if self.obs_all_health: + if not self.stochastic_health: + ally_feats[i, ind] = ( + al_unit.health / al_unit.health_max + ) # health + ind += 1 + elif self.observe_teammate_health: + ally_feats[i, ind] = self._compute_health( + agent_id=al_id, unit=al_unit + ) + ind += 1 + elif self.zero_pad_health: + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_feats[i, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + if self.stochastic_attack and self.observe_attack_probs: + ally_feats[i, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + elif ( + self.stochastic_attack + and self.zero_pad_stochastic_attack + ): + ind += 1 + + if self.stochastic_health and self.observe_teammate_health: + ally_feats[i, ind] = self.agent_health_levels[al_id] + ind += 1 + elif self.stochastic_health and self.zero_pad_health: + ind += 1 + if self.unit_type_bits > 0 and ( + not self.replace_teammates + or self.observe_teammate_types + ): + type_id = self.get_unit_type_id(al_unit, True) + ally_feats[i, ind + type_id] = 1 + ind += self.unit_type_bits + elif self.unit_type_bits > 0 and self.zero_pad_unit_types: + ind += self.unit_type_bits + if self.obs_last_action: + ally_feats[i, ind:] = self.last_action[al_id] + + # Own features + ind = 0 + if self.obs_own_health: + if not self.stochastic_health: + own_feats[ind] = unit.health / unit.health_max + else: + own_feats[ind] = self._compute_health(agent_id, unit) + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(unit) + own_feats[ind] = unit.shield / max_shield + ind += 1 + + if self.stochastic_attack: + own_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + own_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.obs_own_pos: + own_feats[ind] = x / self.map_x + own_feats[ind + 1] = y / self.map_y + ind += 2 + if self.conic_fov: + own_feats[ind : ind + 2] = self.fov_directions[agent_id] + ind += 2 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + own_feats[ind + type_id] = 1 + if self.obs_starcraft: + agent_obs = np.concatenate( + ( + move_feats.flatten(), + enemy_feats.flatten(), + ally_feats.flatten(), + own_feats.flatten(), + ) + ) + + if self.obs_timestep_number: + if self.obs_starcraft: + agent_obs = np.append( + agent_obs, self._episode_steps / self.episode_limit + ) + else: + agent_obs = np.zeros(1, dtype=np.float32) + agent_obs[:] = self._episode_steps / self.episode_limit + + if self.debug: + logging.debug("Obs Agent: {}".format(agent_id).center(60, "-")) + logging.debug( + "Avail. actions {}".format( + self.get_avail_agent_actions(agent_id) + ) + ) + logging.debug("Move feats {}".format(move_feats)) + logging.debug("Enemy feats {}".format(enemy_feats)) + logging.debug("Ally feats {}".format(ally_feats)) + logging.debug("Own feats {}".format(own_feats)) + + return agent_obs + + def get_obs(self): + """Returns all agent observations in a list. + NOTE: Agents should have access only to their local observations + during decentralised execution. + """ + agents_obs = [ + self.get_obs_agent(i, fully_observable=self.fully_observable) + for i in range(self.n_agents) + ] + return agents_obs + + def get_capabilities_agent(self, agent_id): + unit = self.get_unit_by_id(agent_id) + cap_feats = np.zeros(self.get_cap_size(), dtype=np.float32) + + ind = 0 + if self.stochastic_attack: + cap_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + cap_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + cap_feats[ind + type_id] = 1 + + return cap_feats + + def get_capabilities(self): + """Returns all agent capabilities in a list.""" + agents_cap = [ + self.get_capabilities_agent(i) for i in range(self.n_agents) + ] + agents_cap = np.concatenate(agents_cap, axis=0).astype(np.float32) + return agents_cap + + def get_state(self): + """Returns the global state. + NOTE: This function should not be used during decentralised execution. + """ + if self.obs_instead_of_state: + obs_concat = np.concatenate(self.get_obs(), axis=0).astype( + np.float32 + ) + return obs_concat + + state_dict = self.get_state_dict() + + state = np.append( + state_dict["allies"].flatten(), state_dict["enemies"].flatten() + ) + if "last_action" in state_dict: + state = np.append(state, state_dict["last_action"].flatten()) + if "timestep" in state_dict: + state = np.append(state, state_dict["timestep"]) + + state = state.astype(dtype=np.float32) + + if self.debug: + logging.debug("STATE".center(60, "-")) + logging.debug("Ally state {}".format(state_dict["allies"])) + logging.debug("Enemy state {}".format(state_dict["enemies"])) + if self.state_last_action: + logging.debug("Last actions {}".format(self.last_action)) + + return state + + def get_ally_num_attributes(self): + return len(self.ally_state_attr_names) + len( + self.capability_attr_names + ) + + def get_enemy_num_attributes(self): + return len(self.enemy_state_attr_names) + + def get_state_dict(self): + """Returns the global state as a dictionary. + + - allies: numpy array containing agents and their attributes + - enemies: numpy array containing enemies and their attributes + - last_action: numpy array of previous actions for each agent + - timestep: current no. of steps divided by total no. of steps + + NOTE: This function should not be used during decentralised execution. + """ + + # number of features equals the number of attribute names + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + ally_state = np.zeros((self.n_agents, nf_al)) + enemy_state = np.zeros((self.n_enemies, nf_en)) + + center_x = self.map_x / 2 + center_y = self.map_y / 2 + + for al_id, al_unit in self.agents.items(): + if al_unit.health > 0: + x = al_unit.pos.x + y = al_unit.pos.y + max_cd = self.unit_max_cooldown(al_unit) + if not self.stochastic_health: + ally_state[al_id, 0] = ( + al_unit.health / al_unit.health_max + ) # health + else: + ally_state[al_id, 0] = self._compute_health(al_id, al_unit) + if ( + self.map_type in ["MMM", "terran_gen"] + and al_unit.unit_type == self.medivac_id + ): + ally_state[al_id, 1] = al_unit.energy / max_cd # energy + else: + ally_state[al_id, 1] = ( + al_unit.weapon_cooldown / max_cd + ) # cooldown + ally_state[al_id, 2] = ( + x - center_x + ) / self.max_distance_x # relative X + ally_state[al_id, 3] = ( + y - center_y + ) / self.max_distance_y # relative Y + + ind = 4 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_state[al_id, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + + if self.stochastic_attack: + ally_state[al_id, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + if self.stochastic_health: + ally_state[al_id, ind] = self.agent_health_levels[al_id] + ind += 1 + if self.conic_fov: + ally_state[al_id, ind : ind + 2] = self.fov_directions[ + al_id + ] + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(al_unit, True) + ally_state[al_id, type_id - self.unit_type_bits] = 1 + + for e_id, e_unit in self.enemies.items(): + if e_unit.health > 0: + x = e_unit.pos.x + y = e_unit.pos.y + + enemy_state[e_id, 0] = ( + e_unit.health / e_unit.health_max + ) # health + enemy_state[e_id, 1] = ( + x - center_x + ) / self.max_distance_x # relative X + enemy_state[e_id, 2] = ( + y - center_y + ) / self.max_distance_y # relative Y + + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_state[e_id, 3] = e_unit.shield / max_shield # shield + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(e_unit, False) + enemy_state[e_id, type_id - self.unit_type_bits] = 1 + + state = {"allies": ally_state, "enemies": enemy_state} + + if self.state_last_action: + state["last_action"] = self.last_action + if self.state_timestep_number: + state["timestep"] = self._episode_steps / self.episode_limit + + return state + + def get_obs_enemy_feats_size(self): + """Returns the dimensions of the matrix containing enemy features. + Size is n_enemies x n_features. + """ + nf_en = 4 + self.unit_type_bits + + if self.obs_all_health: + nf_en += 1 + self.shield_bits_enemy + + return self.n_enemies, nf_en + + def get_obs_ally_feats_size(self): + """Returns the dimensions of the matrix containing ally features. + Size is n_allies x n_features. + """ + nf_al = 4 + nf_cap = self.get_obs_ally_capability_size() + + if self.obs_all_health: + nf_al += 1 + self.shield_bits_ally + + if self.obs_last_action: + nf_al += self.n_actions + + return self.n_agents - 1, nf_al + nf_cap + + def get_obs_own_feats_size(self): + """ + Returns the size of the vector containing the agents' own features. + """ + own_feats = self.get_cap_size() + if self.obs_own_health and self.obs_starcraft: + own_feats += 1 + self.shield_bits_ally + if self.conic_fov and self.obs_starcraft: + own_feats += 2 + if self.obs_own_pos and self.obs_starcraft: + own_feats += 2 + return own_feats + + def get_obs_move_feats_size(self): + """Returns the size of the vector containing the agents's movement- + related features. + """ + move_feats = self.n_actions_move + if self.obs_pathing_grid: + move_feats += self.n_obs_pathing + if self.obs_terrain_height: + move_feats += self.n_obs_height + + return move_feats + + def get_obs_ally_capability_size(self): + """Returns the size of capabilities observed by teammates.""" + cap_feats = self.unit_type_bits + if self.stochastic_attack and ( + self.zero_pad_stochastic_attack or self.observe_attack_probs + ): + cap_feats += 1 + if self.stochastic_health and ( + self.observe_teammate_health or self.zero_pad_health + ): + cap_feats += 1 + + return cap_feats + + def get_cap_size(self): + """Returns the size of the own capabilities of the agent.""" + cap_feats = 0 + if self.stochastic_attack: + cap_feats += 1 + if self.stochastic_health: + cap_feats += 1 + if self.unit_type_bits > 0: + cap_feats += self.unit_type_bits + + return cap_feats + + def get_obs_size(self): + """Returns the size of the observation.""" + own_feats = self.get_obs_own_feats_size() + move_feats = self.get_obs_move_feats_size() + + n_enemies, n_enemy_feats = self.get_obs_enemy_feats_size() + n_allies, n_ally_feats = self.get_obs_ally_feats_size() + + enemy_feats = n_enemies * n_enemy_feats + ally_feats = n_allies * n_ally_feats + if self.obs_starcraft: + return ( + self.obs_timestep_number + + move_feats + + enemy_feats + + ally_feats + + own_feats + ) + else: + return 1 if self.obs_timestep_number else 0 + + def get_state_size(self): + """Returns the size of the global state.""" + if self.obs_instead_of_state: + return self.get_obs_size() * self.n_agents + + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + enemy_state = self.n_enemies * nf_en + ally_state = self.n_agents * nf_al + + size = enemy_state + ally_state + + if self.state_last_action: + size += self.n_agents * self.n_actions + if self.state_timestep_number: + size += 1 + + return size + + def get_visibility_matrix(self): + """Returns a boolean numpy array of dimensions + (n_agents, n_agents + n_enemies) indicating which units + are visible to each agent. + """ + arr = np.zeros( + (self.n_agents, self.n_agents + self.n_enemies), + dtype=np.bool, + ) + + for agent_id in range(self.n_agents): + current_agent = self.get_unit_by_id(agent_id) + if current_agent.health > 0: # it agent not dead + x = current_agent.pos.x + y = current_agent.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Enemies + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + + if dist < sight_range and e_unit.health > 0: + # visible and alive + arr[agent_id, self.n_agents + e_id] = 1 + + # The matrix for allies is filled symmetrically + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id > agent_id + ] + for _, al_id in enumerate(al_ids): + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + + if dist < sight_range and al_unit.health > 0: + # visible and alive + arr[agent_id, al_id] = arr[al_id, agent_id] = 1 + + return arr + + def get_unit_type_id(self, unit, ally): + """Returns the ID of unit type in the given scenario.""" + + if self.map_type == "protoss_gen": + if unit.unit_type in (self.stalker_id, Protoss.Stalker): + return 0 + if unit.unit_type in (self.zealot_id, Protoss.Zealot): + return 1 + if unit.unit_type in (self.colossus_id, Protoss.Colossus): + return 2 + raise AttributeError() + if self.map_type == "terran_gen": + if unit.unit_type in (self.marine_id, Terran.Marine): + return 0 + if unit.unit_type in (self.marauder_id, Terran.Marauder): + return 1 + if unit.unit_type in (self.medivac_id, Terran.Medivac): + return 2 + raise AttributeError() + + if self.map_type == "zerg_gen": + if unit.unit_type in (self.zergling_id, Zerg.Zergling): + return 0 + if unit.unit_type in (self.hydralisk_id, Zerg.Hydralisk): + return 1 + if unit.unit_type in (self.baneling_id, Zerg.Baneling): + return 2 + raise AttributeError() + + # Old stuff + if ally: # use new SC2 unit types + type_id = unit.unit_type - self._min_unit_type + + if self.map_type == "stalkers_and_zealots": + # id(Stalker) = 74, id(Zealot) = 73 + type_id = unit.unit_type - 73 + elif self.map_type == "colossi_stalkers_zealots": + # id(Stalker) = 74, id(Zealot) = 73, id(Colossus) = 4 + if unit.unit_type == 4: + type_id = 0 + elif unit.unit_type == 74: + type_id = 1 + else: + type_id = 2 + elif self.map_type == "bane": + if unit.unit_type == 9: + type_id = 0 + else: + type_id = 1 + elif self.map_type == "MMM": + if unit.unit_type == 51: + type_id = 0 + elif unit.unit_type == 48: + type_id = 1 + else: + type_id = 2 + + return type_id + + def get_avail_agent_actions(self, agent_id): + """Returns the available actions for agent_id.""" + unit = self.get_unit_by_id(agent_id) + if unit.health > 0: + # cannot choose no-op when alive + avail_actions = [0] * self.n_actions + + # stop should be allowed + avail_actions[1] = 1 + + # see if we can move + if self.can_move(unit, Direction.NORTH): + avail_actions[2] = 1 + if self.can_move(unit, Direction.SOUTH): + avail_actions[3] = 1 + if self.can_move(unit, Direction.EAST): + avail_actions[4] = 1 + if self.can_move(unit, Direction.WEST): + avail_actions[5] = 1 + + if self.conic_fov: + avail_actions[6 : 6 + self.n_fov_actions] = [ + 1 + ] * self.n_fov_actions + + # Can attack only alive units that are alive in the shooting range + shoot_range = self.unit_shoot_range(agent_id) + + target_items = self.enemies.items() + if self.map_type == "MMM" and unit.unit_type == self.medivac_id: + # Medivacs cannot heal themselves or other flying units + target_items = [ + (t_id, t_unit) + for (t_id, t_unit) in self.agents.items() + if t_unit.unit_type != self.medivac_id + ] + # should we only be able to target people in the cone? + for t_id, t_unit in target_items: + if t_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, t_unit.pos.x, t_unit.pos.y + ) + can_shoot = ( + dist <= shoot_range + if not self.conic_fov + else self.is_position_in_cone( + agent_id, t_unit.pos, range="shoot_range" + ) + ) + if can_shoot: + avail_actions[t_id + self.n_actions_no_attack] = 1 + + return avail_actions + + else: + # only no-op allowed + return [1] + [0] * (self.n_actions - 1) + + def get_avail_actions(self): + """Returns the available actions of all agents in a list.""" + avail_actions = [] + for agent_id in range(self.n_agents): + avail_agent = self.get_avail_agent_actions(agent_id) + avail_actions.append(avail_agent) + return avail_actions + + def close(self): + """Close StarCraft II.""" + if self.renderer is not None: + self.renderer.close() + self.renderer = None + if self._sc2_proc: + self._sc2_proc.close() + + def seed(self): + """Returns the random seed used by the environment.""" + return self._seed + + def render(self, mode="human"): + if self.renderer is None: + from smac.env.starcraft2.render import StarCraft2Renderer + + self.renderer = StarCraft2Renderer(self, mode) + assert ( + mode == self.renderer.mode + ), "mode must be consistent across render calls" + return self.renderer.render(mode) + + def _kill_units(self, unit_tags): + debug_command = [ + d_pb.DebugCommand(kill_unit=d_pb.DebugKillUnit(tag=unit_tags)) + ] + self._controller.debug(debug_command) + + def _kill_all_units(self): + """Kill all units on the map. Steps controller and so can throw + exceptions""" + units = [unit.tag for unit in self._obs.observation.raw_data.units] + self._kill_units(units) + # check the units are dead + units = len(self._obs.observation.raw_data.units) + while len(self._obs.observation.raw_data.units) > 0: + self._controller.step(2) + self._obs = self._controller.observe() + + def _create_new_team(self, team, episode_config): + # unit_names = { + # self.id_to_unit_name_map[unit.unit_type] + # for unit in self.agents.values() + # } + # It's important to set the number of agents and enemies + # because we use that to identify whether all the units have + # been created successfully + + # TODO hardcoding init location. change this later for new maps + if not self.random_start: + ally_init_pos = [sc_common.Point2D(x=8, y=16)] * self.n_agents + # Spawning location of enemy units + enemy_init_pos = [sc_common.Point2D(x=24, y=16)] * self.n_enemies + else: + ally_init_pos = [ + sc_common.Point2D( + x=self.ally_start_positions[i][0], + y=self.ally_start_positions[i][1], + ) + for i in range(self.ally_start_positions.shape[0]) + ] + enemy_init_pos = [ + sc_common.Point2D( + x=self.enemy_start_positions[i][0], + y=self.enemy_start_positions[i][1], + ) + for i in range(self.enemy_start_positions.shape[0]) + ] + for unit_id, unit in enumerate(team): + unit_type_ally = self._convert_unit_name_to_unit_type( + unit, ally=True + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_ally, + owner=1, + pos=ally_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + unit_type_enemy = self._convert_unit_name_to_unit_type( + unit, ally=False + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_enemy, + owner=2, + pos=enemy_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def _convert_unit_name_to_unit_type(self, unit_name, ally=True): + if ally: + return self.ally_unit_map[unit_name] + else: + return self.enemy_unit_map[unit_name] + + def init_units(self, team, episode_config={}): + """Initialise the units.""" + if team: + # can use any value for min unit type because + # it is hardcoded based on the version + self._init_ally_unit_types(0) + self._create_new_team(team, episode_config) + while True: + # Sometimes not all units have yet been created by SC2 + self.agents = {} + self.enemies = {} + + ally_units = [ + unit + for unit in self._obs.observation.raw_data.units + if unit.owner == 1 + ] + ally_units_sorted = sorted( + ally_units, + key=attrgetter("unit_type", "pos.x", "pos.y"), + reverse=False, + ) + + for i in range(len(ally_units_sorted)): + self.agents[i] = ally_units_sorted[i] + if self.debug: + logging.debug( + "Unit {} is {}, x = {}, y = {}".format( + len(self.agents), + self.agents[i].unit_type, + self.agents[i].pos.x, + self.agents[i].pos.y, + ) + ) + + for unit in self._obs.observation.raw_data.units: + if unit.owner == 2: + self.enemies[len(self.enemies)] = unit + if self._episode_count == 0: + self.max_reward += unit.health_max + unit.shield_max + + if self._episode_count == 0 and not team: + min_unit_type = min( + unit.unit_type for unit in self.agents.values() + ) + self._init_ally_unit_types(min_unit_type) + + all_agents_created = len(self.agents) == self.n_agents + all_enemies_created = len(self.enemies) == self.n_enemies + + self._unit_types = [ + unit.unit_type for unit in ally_units_sorted + ] + [ + unit.unit_type + for unit in self._obs.observation.raw_data.units + if unit.owner == 2 + ] + + # TODO move this to the start + if all_agents_created and all_enemies_created: # all good + return + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def get_unit_types(self): + if self._unit_types is None: + warn( + "unit types have not been initialized yet, please call" + "env.reset() to populate this and call t1286he method again." + ) + + return self._unit_types + + def update_units(self): + """Update units after an environment step. + This function assumes that self._obs is up-to-date. + """ + n_ally_alive = 0 + n_enemy_alive = 0 + + # Store previous state + self.previous_ally_units = deepcopy(self.agents) + self.previous_enemy_units = deepcopy(self.enemies) + + for al_id, al_unit in self.agents.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if al_unit.tag == unit.tag: + self.agents[al_id] = unit + updated = True + n_ally_alive += 1 + break + + if not updated: # dead + al_unit.health = 0 + + for e_id, e_unit in self.enemies.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if e_unit.tag == unit.tag: + self.enemies[e_id] = unit + updated = True + n_enemy_alive += 1 + break + + if not updated: # dead + e_unit.health = 0 + + if ( + n_ally_alive == 0 + and n_enemy_alive > 0 + or self.only_medivac_left(ally=True) + ): + return -1 # lost + if ( + n_ally_alive > 0 + and n_enemy_alive == 0 + or self.only_medivac_left(ally=False) + ): + return 1 # won + if n_ally_alive == 0 and n_enemy_alive == 0: + return 0 + + return None + + def _register_unit_mapping(self, unit_name, unit_type_id): + self.id_to_unit_name_map[unit_type_id] = unit_name + self.unit_name_to_id_map[unit_name] = unit_type_id + + def _init_ally_unit_types(self, min_unit_type): + """Initialise ally unit types. Should be called once from the + init_units function. + """ + + self._min_unit_type = min_unit_type + + if "10gen_" in self.map_name: + num_rl_units = 9 + self._min_unit_type = ( + len(self._controller.data().units) - num_rl_units + ) + + self.baneling_id = self._min_unit_type + self.colossus_id = self._min_unit_type + 1 + self.hydralisk_id = self._min_unit_type + 2 + self.marauder_id = self._min_unit_type + 3 + self.marine_id = self._min_unit_type + 4 + self.medivac_id = self._min_unit_type + 5 + self.stalker_id = self._min_unit_type + 6 + self.zealot_id = self._min_unit_type + 7 + self.zergling_id = self._min_unit_type + 8 + + self.ally_unit_map = { + "baneling": self.baneling_id, + "colossus": self.colossus_id, + "hydralisk": self.hydralisk_id, + "marauder": self.marauder_id, + "marine": self.marine_id, + "medivac": self.medivac_id, + "stalker": self.stalker_id, + "zealot": self.zealot_id, + "zergling": self.zergling_id, + } + self.enemy_unit_map = { + "baneling": Zerg.Baneling, + "colossus": Protoss.Colossus, + "hydralisk": Zerg.Hydralisk, + "marauder": Terran.Marauder, + "marine": Terran.Marine, + "medivac": Terran.Medivac, + "stalker": Protoss.Stalker, + "zealot": Protoss.Zealot, + "zergling": Zerg.Zergling, + } + + else: + if self.map_type == "marines": + self.marine_id = min_unit_type + self._register_unit_mapping("marine", min_unit_type) + elif self.map_type == "stalkers_and_zealots": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + self.zealot_id = min_unit_type + 1 + self._register_unit_mapping("zealot", min_unit_type + 1) + elif self.map_type == "colossi_stalkers_zealots": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + self.stalker_id = min_unit_type + 1 + self._register_unit_mapping("stalker", min_unit_type + 1) + self.zealot_id = min_unit_type + 2 + self._register_unit_mapping("zealot", min_unit_type + 2) + elif self.map_type == "MMM": + self.marauder_id = min_unit_type + self._register_unit_mapping("marauder", min_unit_type) + self.marine_id = min_unit_type + 1 + self._register_unit_mapping("marine", min_unit_type + 1) + self.medivac_id = min_unit_type + 2 + self._register_unit_mapping("medivac", min_unit_type + 2) + elif self.map_type == "zealots": + self.zealot_id = min_unit_type + self._register_unit_mapping("zealot", min_unit_type) + elif self.map_type == "hydralisks": + self.hydralisk_id = min_unit_type + self._register_unit_mapping("hydralisk", min_unit_type) + elif self.map_type == "stalkers": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + elif self.map_type == "colossus": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + elif self.map_type == "bane": + self.baneling_id = min_unit_type + self._register_unit_mapping("baneling", min_unit_type) + self.zergling_id = min_unit_type + 1 + self._register_unit_mapping("zergling", min_unit_type + 1) + + def only_medivac_left(self, ally): + """Check if only Medivac units are left.""" + if self.map_type != "MMM" and self.map_type != "terran_gen": + return False + + if ally: + units_alive = [ + a + for a in self.agents.values() + if (a.health > 0 and a.unit_type != self.medivac_id) + ] + if len(units_alive) == 0: + return True + return False + else: + units_alive = [ + a + for a in self.enemies.values() + if (a.health > 0 and a.unit_type != Terran.Medivac) + ] + if len(units_alive) == 0: + return True + return False + + def get_unit_by_id(self, a_id): + """Get unit by ID.""" + return self.agents[a_id] + + def get_stats(self): + stats = { + "battles_won": self.battles_won, + "battles_game": self.battles_game, + "battles_draw": self.timeouts, + "win_rate": self.battles_won / self.battles_game, + "timeouts": self.timeouts, + "restarts": self.force_restarts, + } + return stats + + def get_env_info(self): + env_info = super().get_env_info() + env_info["agent_features"] = ( + self.ally_state_attr_names + self.capability_attr_names + ) + env_info["enemy_features"] = self.enemy_state_attr_names + return env_info \ No newline at end of file diff --git a/src/envs/smac_v2/official/smacv2_test.py b/src/envs/smac_v2/official/smacv2_test.py new file mode 100644 index 0000000..516c022 --- /dev/null +++ b/src/envs/smac_v2/official/smacv2_test.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- +''' +@Project ：API-Network +@File ：smacv2_test.py +@Author ：Hao Xiaotian +@Date ：2022/10/15 18:45 +''' + +import sys +import os + +if sys.platform == "linux": + os.environ.setdefault("SC2PATH", + os.path.join(os.path.dirname(os.path.dirname(os.getcwd())), "3rdparty", "StarCraftII")) + +import time + +import numpy as np +from absl import logging +from wrapper import StarCraftCapabilityEnvWrapper + +logging.set_verbosity(logging.DEBUG) + + +def main(): + distribution_config = { + "n_units": 5, + "team_gen": { + "dist_type": "weighted_teams", + "unit_types": ["marine", "marauder", "medivac"], + "exception_unit_types": ["medivac"], + "weights": [0.45, 0.45, 0.1], + "observe": True, + }, + "start_positions": { + "dist_type": "surrounded_and_reflect", + "p": 0.5, + "n_enemies": 5, + "map_x": 32, + "map_y": 32, + }, + } + env = StarCraftCapabilityEnvWrapper( + capability_config=distribution_config, + map_name="10gen_terran", + debug=True, + conic_fov=True, + obs_own_pos=True, + ) + + env_info = env.get_env_info() + + n_actions = env_info["n_actions"] + n_agents = env_info["n_agents"] + + n_episodes = 10 + + print("Training episodes") + for e in range(n_episodes): + env.reset() + terminated = False + episode_reward = 0 + + while not terminated: + obs = env.get_obs() + state = env.get_state() + # env.render() # Uncomment for rendering + + actions = [] + for agent_id in range(n_agents): + avail_actions = env.get_avail_agent_actions(agent_id) + avail_actions_ind = np.nonzero(avail_actions)[0] + action = np.random.choice(avail_actions_ind) + actions.append(action) + + reward, terminated, _ = env.step(actions) + time.sleep(0.15) + episode_reward += reward + print("Total reward in episode {} = {}".format(e, episode_reward)) + + +if __name__ == "__main__": + main() diff --git a/src/envs/smac_v2/official/starcraft2.py b/src/envs/smac_v2/official/starcraft2.py new file mode 100644 index 0000000..ed14128 --- /dev/null +++ b/src/envs/smac_v2/official/starcraft2.py @@ -0,0 +1,2302 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from envs.multiagentenv import MultiAgentEnv +from .maps import get_map_params + +import atexit +from warnings import warn +from operator import attrgetter +from copy import deepcopy +import numpy as np +import enum +import math +from absl import logging +from pysc2.lib.units import Neutral, Protoss, Terran, Zerg + +from pysc2 import maps +from pysc2 import run_configs +from pysc2.lib import protocol + +from s2clientprotocol import common_pb2 as sc_common +from s2clientprotocol import sc2api_pb2 as sc_pb +from s2clientprotocol import raw_pb2 as r_pb +from s2clientprotocol import debug_pb2 as d_pb + +races = { + "R": sc_common.Random, + "P": sc_common.Protoss, + "T": sc_common.Terran, + "Z": sc_common.Zerg, +} + +difficulties = { + "1": sc_pb.VeryEasy, + "2": sc_pb.Easy, + "3": sc_pb.Medium, + "4": sc_pb.MediumHard, + "5": sc_pb.Hard, + "6": sc_pb.Harder, + "7": sc_pb.VeryHard, + "8": sc_pb.CheatVision, + "9": sc_pb.CheatMoney, + "A": sc_pb.CheatInsane, +} + +actions = { + "move": 16, # target: PointOrUnit + "attack": 23, # target: PointOrUnit + "stop": 4, # target: None + "heal": 386, # Unit +} + + +class Direction(enum.IntEnum): + NORTH = 0 + SOUTH = 1 + EAST = 2 + WEST = 3 + + +EPS = 1e-7 + + +class StarCraft2Env(MultiAgentEnv): + """The StarCraft II environment for decentralised multi-agent + micromanagement scenarios. + """ + + def __init__( + self, + map_name="8m", + step_mul=8, + move_amount=2, + difficulty="7", + game_version=None, + seed=None, + continuing_episode=False, + obs_all_health=True, + obs_own_health=True, + obs_last_action=False, + obs_pathing_grid=False, + obs_terrain_height=False, + obs_instead_of_state=False, + obs_timestep_number=False, + obs_own_pos=False, + obs_starcraft=True, + conic_fov=False, + num_fov_actions=12, + state_last_action=True, + state_timestep_number=False, + reward_sparse=False, + reward_only_positive=True, + reward_death_value=10, + reward_win=200, + reward_defeat=0, + reward_negative_scale=0.5, + reward_scale=True, + reward_scale_rate=20, + kill_unit_step_mul=2, + fully_observable=False, + capability_config={}, + replay_dir="", + replay_prefix="", + window_size_x=1920, + window_size_y=1200, + heuristic_ai=False, + heuristic_rest=False, + debug=False, + ): + """ + Create a StarCraftC2Env environment. + + Parameters + ---------- + map_name : str, optional + The name of the SC2 map to play (default is "8m"). The full list + can be found by running bin/map_list. + step_mul : int, optional + How many game steps per agent step (default is 8). None + indicates to use the default map step_mul. + move_amount : float, optional + How far away units are ordered to move per step (default is 2). + difficulty : str, optional + The difficulty of built-in computer AI bot (default is "7"). + game_version : str, optional + StarCraft II game version (default is None). None indicates the + latest version. + seed : int, optional + Random seed used during game initialisation. This allows to + continuing_episode : bool, optional + Whether to consider episodes continuing or finished after time + limit is reached (default is False). + obs_all_health : bool, optional + Agents receive the health of all units (in the sight range) as part + of observations (default is True). + obs_own_health : bool, optional + Agents receive their own health as a part of observations (default + is False). This flag is ignored when obs_all_health == True. + obs_last_action : bool, optional + Agents receive the last actions of all units (in the sight range) + as part of observations (default is False). + obs_pathing_grid : bool, optional + Whether observations include pathing values surrounding the agent + (default is False). + obs_terrain_height : bool, optional + Whether observations include terrain height values surrounding the + agent (default is False). + obs_instead_of_state : bool, optional + Use combination of all agents' observations as the global state + (default is False). + obs_timestep_number : bool, optional + Whether observations include the current timestep of the episode + (default is False). + state_last_action : bool, optional + Include the last actions of all agents as part of the global state + (default is True). + state_timestep_number : bool, optional + Whether the state include the current timestep of the episode + (default is False). + reward_sparse : bool, optional + Receive 1/-1 reward for winning/loosing an episode (default is + False). Whe rest of reward parameters are ignored if True. + reward_only_positive : bool, optional + Reward is always positive (default is True). + reward_death_value : float, optional + The amount of reward received for killing an enemy unit (default + is 10). This is also the negative penalty for having an allied unit + killed if reward_only_positive == False. + reward_win : float, optional + The reward for winning in an episode (default is 200). + reward_defeat : float, optional + The reward for loosing in an episode (default is 0). This value + should be nonpositive. + reward_negative_scale : float, optional + Scaling factor for negative rewards (default is 0.5). This + parameter is ignored when reward_only_positive == True. + reward_scale : bool, optional + Whether or not to scale the reward (default is True). + reward_scale_rate : float, optional + Reward scale rate (default is 20). When reward_scale == True, the + reward received by the agents is divided by (max_reward / + reward_scale_rate), where max_reward is the maximum possible + reward per episode without considering the shield regeneration + of Protoss units. + replay_dir : str, optional + The directory to save replays (default is None). If None, the + replay will be saved in Replays directory where StarCraft II is + installed. + replay_prefix : str, optional + The prefix of the replay to be saved (default is None). If None, + the name of the map will be used. + window_size_x : int, optional + The length of StarCraft II window size (default is 1920). + window_size_y: int, optional + The height of StarCraft II window size (default is 1200). + heuristic_ai: bool, optional + Whether or not to use a non-learning heuristic AI (default False). + heuristic_rest: bool, optional + At any moment, restrict the actions of the heuristic AI to be + chosen from actions available to RL agents (default is False). + Ignored if heuristic_ai == False. + debug: bool, optional + Log messages about observations, state, actions and rewards for + debugging purposes (default is False). + """ + # Map arguments + self.map_name = map_name + map_params = get_map_params(self.map_name) + self.map_params = map_params + self.episode_limit = map_params["limit"] + self._move_amount = move_amount + self._step_mul = step_mul + self._kill_unit_step_mul = kill_unit_step_mul + self.difficulty = difficulty + + # Observations and state + self.obs_own_health = obs_own_health + self.obs_all_health = obs_all_health + self.obs_instead_of_state = obs_instead_of_state + self.obs_last_action = obs_last_action + self.obs_pathing_grid = obs_pathing_grid + self.obs_terrain_height = obs_terrain_height + self.obs_timestep_number = obs_timestep_number + self.obs_starcraft = obs_starcraft + self.state_last_action = state_last_action + self.state_timestep_number = state_timestep_number + if self.obs_all_health: + self.obs_own_health = True + self.n_obs_pathing = 8 + self.n_obs_height = 9 + + # Rewards args + self.reward_sparse = reward_sparse + self.reward_only_positive = reward_only_positive + self.reward_negative_scale = reward_negative_scale + self.reward_death_value = reward_death_value + self.reward_win = reward_win + self.reward_defeat = reward_defeat + self.reward_scale = reward_scale + self.reward_scale_rate = reward_scale_rate + + # Meta MARL + self.capability_config = capability_config + self.fully_observable = fully_observable + self.stochastic_attack = "attack" in self.capability_config + self.stochastic_health = "health" in self.capability_config + self.replace_teammates = "team_gen" in self.capability_config + self.obs_own_pos = obs_own_pos + self.mask_enemies = "enemy_mask" in self.capability_config + if self.stochastic_attack: + self.zero_pad_stochastic_attack = not self.capability_config[ + "attack" + ]["observe"] + self.observe_attack_probs = self.capability_config["attack"][ + "observe" + ] + if self.stochastic_health: + self.zero_pad_health = not self.capability_config["health"][ + "observe" + ] + self.observe_teammate_health = self.capability_config["health"][ + "observe" + ] + if self.replace_teammates: + self.zero_pad_unit_types = not self.capability_config["team_gen"][ + "observe" + ] + self.observe_teammate_types = self.capability_config["team_gen"][ + "observe" + ] + self.n_agents = ( + map_params["n_agents"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.n_enemies = ( + map_params["n_enemies"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.random_start = "start_positions" in self.capability_config + self.conic_fov = conic_fov + self.n_fov_actions = num_fov_actions if self.conic_fov else 0 + self.conic_fov_angle = ( + (2 * np.pi) / self.n_fov_actions if self.conic_fov else 0 + ) + # Other + self.game_version = game_version + self.continuing_episode = continuing_episode + self._seed = seed + self.heuristic_ai = heuristic_ai + self.heuristic_rest = heuristic_rest + self.debug = debug + self.window_size = (window_size_x, window_size_y) + self.replay_dir = replay_dir + self.replay_prefix = replay_prefix + + # Actions + self.n_actions_move = 4 + + self.n_actions_no_attack = self.n_actions_move + self.n_fov_actions + 2 + self.n_actions = self.n_actions_no_attack + self.n_enemies + + # Map info + self._agent_race = map_params["a_race"] + self._bot_race = map_params["b_race"] + self.shield_bits_ally = 1 if self._agent_race == "P" else 0 + self.shield_bits_enemy = 1 if self._bot_race == "P" else 0 + # NOTE: The map_type, which is used to initialise the unit + # type ids, the unit_type_bits and the races, are still properties of the + # map. This means even the 10gen_{race} maps are limited to the + # unit types statically defined in the unit type id assignment. + # Lifting this restriction shouldn't be too much work, I've just + # not done it. + self.unit_type_bits = map_params["unit_type_bits"] + self.map_type = map_params["map_type"] + self._unit_types = None + + self.max_reward = ( + self.n_enemies * self.reward_death_value + self.reward_win + ) + + # create lists containing the names of attributes returned in states + self.ally_state_attr_names = [ + "health", + "energy/cooldown", + "rel_x", + "rel_y", + ] + self.enemy_state_attr_names = ["health", "rel_x", "rel_y"] + + if self.shield_bits_ally > 0: + self.ally_state_attr_names += ["shield"] + if self.shield_bits_enemy > 0: + self.enemy_state_attr_names += ["shield"] + if self.conic_fov: + self.ally_state_attr_names += ["fov_x", "fov_y"] + + self.capability_attr_names = [] + if "attack" in self.capability_config: + self.capability_attr_names += ["attack_probability"] + if "health" in self.capability_config: + self.capability_attr_names += ["total_health"] + if self.unit_type_bits > 0: + bit_attr_names = [ + "type_{}".format(bit) for bit in range(self.unit_type_bits) + ] + self.capability_attr_names += bit_attr_names + self.enemy_state_attr_names += bit_attr_names + + self.agents = {} + self.enemies = {} + self.unit_name_to_id_map = {} + self.id_to_unit_name_map = {} + self._episode_count = 0 + self._episode_steps = 0 + self._total_steps = 0 + self._obs = None + self.battles_won = 0 + self.battles_game = 0 + self.timeouts = 0 + self.force_restarts = 0 + self.last_stats = None + self.agent_attack_probabilities = np.zeros(self.n_agents) + self.agent_health_levels = np.zeros(self.n_agents) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.canonical_fov_directions = np.array( + [ + ( + np.cos(2 * np.pi * (i / self.n_fov_actions)), + np.sin(2 * np.pi * (i / self.n_fov_actions)), + ) + for i in range(self.n_fov_actions) + ] + ) + self.new_unit_positions = np.zeros((self.n_agents, 2)) + self.previous_ally_units = None + self.previous_enemy_units = None + self.last_action = np.zeros((self.n_agents, self.n_actions)) + self.init_positions = np.zeros((self.n_agents, 2)) + self._min_unit_type = 0 + self.marine_id = self.marauder_id = self.medivac_id = 0 + self.hydralisk_id = self.zergling_id = self.baneling_id = 0 + self.stalker_id = self.colossus_id = self.zealot_id = 0 + self.max_distance_x = 0 + self.max_distance_y = 0 + self.map_x = 0 + self.map_y = 0 + self.reward = 0 + self.renderer = None + self.terrain_height = None + self.pathing_grid = None + self._run_config = None + self._sc2_proc = None + self._controller = None + # Try to avoid leaking SC2 processes on shutdown + atexit.register(lambda: self.close()) + + def _only_one_meta_marl_flag_on(self): + """Function that checks that either all the meta marl flags are off, + or at most one has been enabled.""" + if self.stochastic_attack: + return not self.stochastic_health and not self.replace_teammates + else: + return not self.replace_teammates or not self.stochastic_health + + def _launch(self): + """Launch the StarCraft II game.""" + self._run_config = run_configs.get(version=self.game_version) + self.version = self._run_config.version + _map = maps.get(self.map_name) + + # Setting up the interface + interface_options = sc_pb.InterfaceOptions(raw=True, score=False) + self._sc2_proc = self._run_config.start( + window_size=self.window_size, want_rgb=False + ) + self._controller = self._sc2_proc.controller + + # Request to create the game + create = sc_pb.RequestCreateGame( + local_map=sc_pb.LocalMap( + map_path=_map.path, + map_data=self._run_config.map_data(_map.path), + ), + realtime=False, + random_seed=self._seed, + ) + create.player_setup.add(type=sc_pb.Participant) + create.player_setup.add( + type=sc_pb.Computer, + race=races[self._bot_race], + difficulty=difficulties[self.difficulty], + ) + self._controller.create_game(create) + + join = sc_pb.RequestJoinGame( + race=races[self._agent_race], options=interface_options + ) + self._controller.join_game(join) + + game_info = self._controller.game_info() + map_info = game_info.start_raw + self.map_play_area_min = map_info.playable_area.p0 + self.map_play_area_max = map_info.playable_area.p1 + self.max_distance_x = ( + self.map_play_area_max.x - self.map_play_area_min.x + ) + self.max_distance_y = ( + self.map_play_area_max.y - self.map_play_area_min.y + ) + self.map_x = map_info.map_size.x + self.map_y = map_info.map_size.y + + if map_info.pathing_grid.bits_per_pixel == 1: + vals = np.array(list(map_info.pathing_grid.data)).reshape( + self.map_x, int(self.map_y / 8) + ) + self.pathing_grid = np.transpose( + np.array( + [ + [(b >> i) & 1 for b in row for i in range(7, -1, -1)] + for row in vals + ], + dtype=np.bool, + ) + ) + else: + self.pathing_grid = np.invert( + np.flip( + np.transpose( + np.array( + list(map_info.pathing_grid.data), dtype=np.bool + ).reshape(self.map_x, self.map_y) + ), + axis=1, + ) + ) + + self.terrain_height = ( + np.flip( + np.transpose( + np.array(list(map_info.terrain_height.data)).reshape( + self.map_x, self.map_y + ) + ), + 1, + ) + / 255 + ) + + def reset(self, episode_config={}): + """Reset the environment. Required after each full episode. + Returns initial observations and states. + """ + self._episode_steps = 0 + self.episode_config = episode_config + if self._episode_count == 0: + # Launch StarCraft II + self._launch() + else: + self._restart() + + # Information kept for counting the reward + self.agent_attack_probabilities = episode_config.get("attack", {}).get( + "item", None + ) + self.agent_health_levels = episode_config.get("health", {}).get( + "item", None + ) + self.enemy_mask = episode_config.get("enemy_mask", {}).get( + "item", None + ) + self.ally_start_positions = episode_config.get( + "ally_start_positions", {} + ).get("item", None) + self.enemy_start_positions = episode_config.get( + "enemy_start_positions", {} + ).get("item", None) + self.mask_enemies = self.enemy_mask is not None + team = episode_config.get("team_gen", {}).get("item", None) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.previous_ally_units = None + self.previous_enemy_units = None + self.win_counted = False + self.defeat_counted = False + if self.debug: + logging.debug( + "Attack Probabilities: {}".format(self.agent_attack_probabilities) + ) + logging.debug("Health Levels: {}".format(self.agent_health_levels)) + self.last_action = np.zeros((self.n_agents, self.n_actions)) + + if self.heuristic_ai: + self.heuristic_targets = [None] * self.n_agents + + try: + self._obs = self._controller.observe() + self.init_units(team, episode_config=episode_config) + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + if self.debug: + logging.debug( + "Started Episode {}".format(self._episode_count).center( + 60, "*" + ) + ) + return self.get_obs(), self.get_state() + + def _restart(self): + """Restart the environment by killing all units on the map. + There is a trigger in the SC2Map file, which restarts the + episode when there are no units left. + """ + try: + self._kill_all_units() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + def full_restart(self): + """Full restart. Closes the SC2 process and launches a new one.""" + self._sc2_proc.close() + self._launch() + self.force_restarts += 1 + + def _kill_units_below_health_level(self): + units_to_kill = [] + for al_id, al_unit in self.agents.items(): + if ( + al_unit.health / al_unit.health_max + < self.agent_health_levels[al_id] + ) and not self.death_tracker_ally[al_id]: + units_to_kill.append(al_unit.tag) + self._kill_units(units_to_kill) + + def step(self, actions): + """A single environment step. Returns reward, terminated, info.""" + actions_int = [int(a) for a in actions] + + self.last_action = np.eye(self.n_actions)[np.array(actions_int)] + + # Collect individual actions + sc_actions = [] + if self.debug: + logging.debug("Actions".center(60, "-")) + + for a_id, action in enumerate(actions_int): + if not self.heuristic_ai: + sc_action = self.get_agent_action(a_id, action) + else: + sc_action, action_num = self.get_agent_action_heuristic( + a_id, action + ) + actions[a_id] = action_num + if sc_action: + sc_actions.append(sc_action) + # Send action request + req_actions = sc_pb.RequestAction(actions=sc_actions) + + try: + if self.conic_fov: + self.render_fovs() + self._controller.actions(req_actions) + # Make step in SC2, i.e. apply actions + if not self.stochastic_health: + self._controller.step(self._step_mul) + else: + self._controller.step( + self._step_mul - self._kill_unit_step_mul + ) + self._kill_units_below_health_level() + self._controller.step(self._kill_unit_step_mul) + # Observe here so that we know if the episode is over. + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + return 0, True, {} + + self._total_steps += 1 + self._episode_steps += 1 + + # Update units + game_end_code = self.update_units() + + terminated = False + reward = self.reward_battle() + info = {"battle_won": False} + + # count units that are still alive + dead_allies, dead_enemies = 0, 0 + for _al_id, al_unit in self.agents.items(): + if al_unit.health == 0: + dead_allies += 1 + for _e_id, e_unit in self.enemies.items(): + if e_unit.health == 0: + dead_enemies += 1 + + info["dead_allies"] = dead_allies + info["dead_enemies"] = dead_enemies + + if game_end_code is not None: + # Battle is over + terminated = True + self.battles_game += 1 + if game_end_code == 1 and not self.win_counted: + self.battles_won += 1 + self.win_counted = True + info["battle_won"] = True + if not self.reward_sparse: + reward += self.reward_win + else: + reward = 1 + elif game_end_code == -1 and not self.defeat_counted: + self.defeat_counted = True + if not self.reward_sparse: + reward += self.reward_defeat + else: + reward = -1 + + elif self._episode_steps >= self.episode_limit: + # Episode limit reached + terminated = True + if self.continuing_episode: + info["episode_limit"] = True + self.battles_game += 1 + self.timeouts += 1 + + if self.debug: + logging.debug("Reward = {}".format(reward).center(60, "-")) + + if terminated: + self._episode_count += 1 + + if self.reward_scale: + reward /= self.max_reward / self.reward_scale_rate + + self.reward = reward + + return reward, terminated, info + + def get_agent_action(self, a_id, action): + """Construct the action for agent a_id.""" + avail_actions = self.get_avail_agent_actions(a_id) + assert ( + avail_actions[action] == 1 + ), "Agent {} cannot perform action {}".format(a_id, action) + + unit = self.get_unit_by_id(a_id) + tag = unit.tag + x = unit.pos.x + y = unit.pos.y + + if action == 0: + # no-op (valid only when dead) + assert unit.health == 0, "No-op only available for dead agents." + if self.debug: + logging.debug("Agent {}: Dead".format(a_id)) + return None + elif action == 1: + # stop + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["stop"], + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Stop".format(a_id)) + + elif action == 2: + # move north + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y + self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y + self._move_amount] + ) + if self.debug: + logging.debug("Agent {}: Move North".format(a_id)) + + elif action == 3: + # move south + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y - self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y - self._move_amount] + ) + if self.debug: + logging.debug("Agent {}: Move South".format(a_id)) + + elif action == 4: + # move east + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x + self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x + self._move_amount, y] + ) + + if self.debug: + logging.debug("Agent {}: Move East".format(a_id)) + + elif action == 5: + # move west + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x - self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x - self._move_amount, y] + ) + if self.debug: + logging.debug("Agent {}: Move West".format(a_id)) + elif self.conic_fov and action in range(6, 6 + self.n_fov_actions): + self.fov_directions[a_id] = self.canonical_fov_directions[ + action - 6 + ] + cmd = None + else: + # attack/heal units that are in range + target_id = action - self.n_actions_no_attack + if ( + self.map_type in ["MMM", "terran_gen"] + and unit.unit_type == self.medivac_id + ): + target_unit = self.agents[target_id] + action_name = "heal" + else: + target_unit = self.enemies[target_id] + action_name = "attack" + + if self.stochastic_attack: + p = np.random.default_rng().uniform() + if p > self.agent_attack_probabilities[a_id]: + if self.debug: + logging.debug( + "Agent {} {}s {}, but fails".format(a_id, action_name, target_id) + ) + return None + action_id = actions[action_name] + target_tag = target_unit.tag + + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + if self.debug: + logging.debug( + "Agent {} {}s unit # {}".format( + a_id, action_name, target_id + ) + ) + if cmd: + sc_action = sc_pb.Action( + action_raw=r_pb.ActionRaw(unit_command=cmd) + ) + return sc_action + return None + + def get_agent_action_heuristic(self, a_id, action): + unit = self.get_unit_by_id(a_id) + tag = unit.tag + + target = self.heuristic_targets[a_id] + if unit.unit_type == self.medivac_id: + if ( + target is None + or self.agents[target].health == 0 + or self.agents[target].health == self.agents[target].health_max + ): + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for al_id, al_unit in self.agents.items(): + if al_unit.unit_type == self.medivac_id: + continue + if ( + al_unit.health != 0 + and al_unit.health != al_unit.health_max + ): + dist = self.distance( + unit.pos.x, + unit.pos.y, + al_unit.pos.x, + al_unit.pos.y, + ) + if dist < min_dist: + min_dist = dist + min_id = al_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["heal"] + target_tag = self.agents[self.heuristic_targets[a_id]].tag + else: + if target is None or self.enemies[target].health == 0: + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for e_id, e_unit in self.enemies.items(): + if ( + unit.unit_type == self.marauder_id + and e_unit.unit_type == self.medivac_id + ): + continue + if e_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, e_unit.pos.x, e_unit.pos.y + ) + if dist < min_dist: + min_dist = dist + min_id = e_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["attack"] + target_tag = self.enemies[self.heuristic_targets[a_id]].tag + + action_num = self.heuristic_targets[a_id] + self.n_actions_no_attack + + # Check if the action is available + if ( + self.heuristic_rest + and self.get_avail_agent_actions(a_id)[action_num] == 0 + ): + + # Move towards the target rather than attacking/healing + if unit.unit_type == self.medivac_id: + target_unit = self.agents[self.heuristic_targets[a_id]] + else: + target_unit = self.enemies[self.heuristic_targets[a_id]] + + delta_x = target_unit.pos.x - unit.pos.x + delta_y = target_unit.pos.y - unit.pos.y + + if abs(delta_x) > abs(delta_y): # east or west + if delta_x > 0: # east + target_pos = sc_common.Point2D( + x=unit.pos.x + self._move_amount, y=unit.pos.y + ) + action_num = 4 + else: # west + target_pos = sc_common.Point2D( + x=unit.pos.x - self._move_amount, y=unit.pos.y + ) + action_num = 5 + else: # north or south + if delta_y > 0: # north + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y + self._move_amount + ) + action_num = 2 + else: # south + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y - self._move_amount + ) + action_num = 3 + + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=target_pos, + unit_tags=[tag], + queue_command=False, + ) + else: + # Attack/heal the target + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd)) + return sc_action, action_num + + def reward_battle(self): + """Reward function when self.reward_spare==False. + Returns accumulative hit/shield point damage dealt to the enemy + + reward_death_value per enemy unit killed, and, in case + self.reward_only_positive == False, - (damage dealt to ally units + + reward_death_value per ally unit killed) * self.reward_negative_scale + """ + assert ( + not self.stochastic_health or self.reward_only_positive + ), "Different Health Levels are currently only compatible with positive rewards" + if self.reward_sparse: + return 0 + + reward = 0 + delta_deaths = 0 + delta_ally = 0 + delta_enemy = 0 + + neg_scale = self.reward_negative_scale + + # update deaths + for al_id, al_unit in self.agents.items(): + if not self.death_tracker_ally[al_id]: + # did not die so far + prev_health = ( + self.previous_ally_units[al_id].health + + self.previous_ally_units[al_id].shield + ) + if al_unit.health == 0: + # just died + self.death_tracker_ally[al_id] = 1 + if not self.reward_only_positive: + delta_deaths -= self.reward_death_value * neg_scale + delta_ally += prev_health * neg_scale + else: + # still alive + delta_ally += neg_scale * ( + prev_health - al_unit.health - al_unit.shield + ) + + for e_id, e_unit in self.enemies.items(): + if not self.death_tracker_enemy[e_id]: + prev_health = ( + self.previous_enemy_units[e_id].health + + self.previous_enemy_units[e_id].shield + ) + if e_unit.health == 0: + self.death_tracker_enemy[e_id] = 1 + delta_deaths += self.reward_death_value + delta_enemy += prev_health + else: + delta_enemy += prev_health - e_unit.health - e_unit.shield + + if self.reward_only_positive: + reward = max(delta_enemy + delta_deaths, 0) # shield regeneration + else: + reward = delta_enemy + delta_deaths - delta_ally + + return reward + + def get_total_actions(self): + """Returns the total number of actions an agent could ever take.""" + return self.n_actions + + @staticmethod + def distance(x1, y1, x2, y2): + """Distance between two points.""" + return math.hypot(x2 - x1, y2 - y1) + + def unit_shoot_range(self, agent_id): + """Returns the shooting range for an agent.""" + return 6 + + def unit_sight_range(self, agent_id): + """Returns the sight range for an agent.""" + return 9 + + def unit_max_cooldown(self, unit): + """Returns the maximal cooldown for a unit.""" + switcher = { + self.marine_id: 15, + self.marauder_id: 25, + self.medivac_id: 200, # max energy + self.stalker_id: 35, + self.zealot_id: 22, + self.colossus_id: 24, + self.hydralisk_id: 10, + self.zergling_id: 11, + self.baneling_id: 1, + } + return switcher.get(unit.unit_type, 15) + + def save_replay(self): + """Save a replay.""" + prefix = self.replay_prefix or self.map_name + replay_dir = self.replay_dir or "" + replay_path = self._run_config.save_replay( + self._controller.save_replay(), + replay_dir=replay_dir, + prefix=prefix, + ) + logging.info("Replay saved at: %s" % replay_path) + + def unit_max_shield(self, unit): + """Returns maximal shield for a given unit.""" + if unit.unit_type == 74 or unit.unit_type == self.stalker_id: + return 80 # Protoss's Stalker + elif unit.unit_type == 73 or unit.unit_type == self.zealot_id: + return 50 # Protoss's Zealot + elif unit.unit_type == 4 or unit.unit_type == self.colossus_id: + return 150 # Protoss's Colossus + else: + raise Exception("Maximum shield not recognised") + + def can_move(self, unit, direction): + """Whether a unit can move in a given direction.""" + m = self._move_amount / 2 + + if direction == Direction.NORTH: + x, y = int(unit.pos.x), int(unit.pos.y + m) + elif direction == Direction.SOUTH: + x, y = int(unit.pos.x), int(unit.pos.y - m) + elif direction == Direction.EAST: + x, y = int(unit.pos.x + m), int(unit.pos.y) + else: + x, y = int(unit.pos.x - m), int(unit.pos.y) + + if self.check_bounds(x, y) and self.pathing_grid[x, y]: + return True + + return False + + def get_surrounding_points(self, unit, include_self=False): + """Returns the surrounding points of the unit in 8 directions.""" + x = int(unit.pos.x) + y = int(unit.pos.y) + + ma = self._move_amount + + points = [ + (x, y + 2 * ma), + (x, y - 2 * ma), + (x + 2 * ma, y), + (x - 2 * ma, y), + (x + ma, y + ma), + (x - ma, y - ma), + (x + ma, y - ma), + (x - ma, y + ma), + ] + + if include_self: + points.append((x, y)) + + return points + + def check_bounds(self, x, y): + """Whether a point is within the map bounds.""" + return 0 <= x < self.map_x and 0 <= y < self.map_y + + def get_surrounding_pathing(self, unit): + """Returns pathing values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=False) + vals = [ + self.pathing_grid[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def get_surrounding_height(self, unit): + """Returns height values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=True) + vals = [ + self.terrain_height[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def _compute_health(self, agent_id, unit): + """Each agent has a health bar with max health + `health_max` and current health `health`. We set a level + `health_level` between `0` and `1` where the agent dies if its + proportional health (`health / health_max`) is below that level. + This function rescales health to take into account this death level. + + In the proportional health scale we have something that looks like this: + + ------------------------------------------------------------- + 0 1 + ^ health_level ^ proportional_health + And so we compute + (proportional_health - health_level) / (1 - health_level) + """ + proportional_health = unit.health / unit.health_max + health_level = self.agent_health_levels[agent_id] + return (1.0 / (1 - health_level)) * ( + proportional_health - health_level + ) + + def render_fovs(self): + lines_to_render = [] + for agent_id in range(self.n_agents): + if not self.death_tracker_ally[agent_id]: + lines_to_render.extend(self.agent_cone(agent_id)) + debug_command = d_pb.DebugCommand( + draw=d_pb.DebugDraw(lines=lines_to_render) + ) + self._controller.debug(debug_command) + + def agent_cone(self, agent_id): + fov_direction = self.fov_directions[agent_id] + c, s = np.cos(self.conic_fov_angle / 2), np.sin( + self.conic_fov_angle / 2 + ) + sight_range = self.unit_sight_range(agent_id) + rot = np.array([[c, -s], [s, c]]) # Contra Rotate + neg_rot = np.array([[c, s], [-s, c]]) # Rotate Clockwise + start_pos = self.new_unit_positions[agent_id] + init_pos = sc_common.Point( + x=start_pos[0], + y=start_pos[1], + z=self.get_unit_by_id(agent_id).pos.z, + ) + upper_cone_end = start_pos + (rot @ fov_direction) * sight_range + lower_cone_end = start_pos + (neg_rot @ fov_direction) * sight_range + lines = [ + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=upper_cone_end[0], + y=upper_cone_end[1], + z=init_pos.z, + ), + ) + ), + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=lower_cone_end[0], + y=lower_cone_end[1], + z=init_pos.z, + ), + ) + ), + ] + return lines + + def is_position_in_cone(self, agent_id, pos, range="sight_range"): + ally_pos = self.get_unit_by_id(agent_id).pos + distance = self.distance(ally_pos.x, ally_pos.y, pos.x, pos.y) + # position is in this agent's cone if it is not outside the sight + # range and has the correct angle + if range == "sight_range": + unit_range = self.unit_sight_range(agent_id) + elif range == "shoot_range": + unit_range = self.unit_shoot_range(agent_id) + else: + raise Exception("Range argument not recognised") + if distance > unit_range: + return False + x_diff = pos.x - ally_pos.x + x_diff = max(x_diff, EPS) if x_diff > 0 else min(x_diff, -EPS) + obj_angle = np.arctan((pos.y - ally_pos.y) / x_diff) + x = self.fov_directions[agent_id][0] + x = max(x, EPS) if x_diff > 0 else min(x, -EPS) + fov_angle = np.arctan(self.fov_directions[agent_id][1] / x) + return np.abs(obj_angle - fov_angle) < self.conic_fov_angle / 2 + + def get_obs_agent(self, agent_id, fully_observable=False): + """Returns observation for agent_id. The observation is composed of: + + - agent movement features (where it can move to, height information + and pathing grid) + - enemy features (available_to_attack, health, relative_x, relative_y, + shield, unit_type) + - ally features (visible, distance, relative_x, relative_y, shield, + unit_type) + - agent unit features (health, shield, unit_type) + + All of this information is flattened and concatenated into a list, + in the aforementioned order. To know the sizes of each of the + features inside the final list of features, take a look at the + functions ``get_obs_move_feats_size()``, + ``get_obs_enemy_feats_size()``, ``get_obs_ally_feats_size()`` and + ``get_obs_own_feats_size()``. + + The size of the observation vector may vary, depending on the + environment configuration and type of units present in the map. + For instance, non-Protoss units will not have shields, movement + features may or may not include terrain height and pathing grid, + unit_type is not included if there is only one type of unit in the + map etc.). + + NOTE: Agents should have access only to their local observations + during decentralised execution. + + fully_observable: -- ignores sight range for a particular unit. + For Debugging purposes ONLY -- not a fair observation. + """ + unit = self.get_unit_by_id(agent_id) + + move_feats_dim = self.get_obs_move_feats_size() + enemy_feats_dim = self.get_obs_enemy_feats_size() + ally_feats_dim = self.get_obs_ally_feats_size() + own_feats_dim = self.get_obs_own_feats_size() + + move_feats = np.zeros(move_feats_dim, dtype=np.float32) + enemy_feats = np.zeros(enemy_feats_dim, dtype=np.float32) + ally_feats = np.zeros(ally_feats_dim, dtype=np.float32) + own_feats = np.zeros(own_feats_dim, dtype=np.float32) + + if ( + unit.health > 0 and self.obs_starcraft + ): # otherwise dead, return all zeros + x = unit.pos.x + y = unit.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Movement features. Do not need similar for looking + # around because this is always possible + avail_actions = self.get_avail_agent_actions(agent_id) + for m in range(self.n_actions_move): + move_feats[m] = avail_actions[m + 2] + + ind = self.n_actions_move + + if self.obs_pathing_grid: + move_feats[ + ind: ind + self.n_obs_pathing # noqa + ] = self.get_surrounding_pathing(unit) + ind += self.n_obs_pathing + + if self.obs_terrain_height: + move_feats[ind:] = self.get_surrounding_height(unit) + + # Enemy features + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + enemy_visible = ( + self.is_position_in_cone(agent_id, e_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (enemy_visible and e_unit.health > 0) or ( + e_unit.health > 0 and fully_observable + ): # visible and alive + # Sight range > shoot range + enemy_feats[e_id, 0] = avail_actions[ + self.n_actions_no_attack + e_id + ] # available + enemy_feats[e_id, 1] = dist / sight_range # distance + enemy_feats[e_id, 2] = ( + e_x - x + ) / sight_range # relative X + enemy_feats[e_id, 3] = ( + e_y - y + ) / sight_range # relative Y + show_enemy = ( + self.mask_enemies + and not self.enemy_mask[agent_id][e_id] + ) or not self.mask_enemies + ind = 4 + if self.obs_all_health and show_enemy: + enemy_feats[e_id, ind] = ( + e_unit.health / e_unit.health_max + ) # health + ind += 1 + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_feats[e_id, ind] = ( + e_unit.shield / max_shield + ) # shield + ind += 1 + + if self.unit_type_bits > 0 and show_enemy: + type_id = self.get_unit_type_id(e_unit, False) + enemy_feats[e_id, ind + type_id] = 1 # unit type + + # Ally features + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id != agent_id + ] + for i, al_id in enumerate(al_ids): + + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + ally_visible = ( + self.is_position_in_cone(agent_id, al_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (ally_visible and al_unit.health > 0) or ( + al_unit.health > 0 and fully_observable + ): # visible and alive + ally_feats[i, 0] = 1 # visible + ally_feats[i, 1] = dist / sight_range # distance + ally_feats[i, 2] = (al_x - x) / sight_range # relative X + ally_feats[i, 3] = (al_y - y) / sight_range # relative Y + + ind = 4 + if self.obs_all_health: + if not self.stochastic_health: + ally_feats[i, ind] = ( + al_unit.health / al_unit.health_max + ) # health + ind += 1 + elif self.observe_teammate_health: + ally_feats[i, ind] = self._compute_health( + agent_id=al_id, unit=al_unit + ) + ind += 1 + elif self.zero_pad_health: + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_feats[i, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + if self.stochastic_attack and self.observe_attack_probs: + ally_feats[i, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + elif ( + self.stochastic_attack + and self.zero_pad_stochastic_attack + ): + ind += 1 + + if self.stochastic_health and self.observe_teammate_health: + ally_feats[i, ind] = self.agent_health_levels[al_id] + ind += 1 + elif self.stochastic_health and self.zero_pad_health: + ind += 1 + if self.unit_type_bits > 0 and ( + not self.replace_teammates + or self.observe_teammate_types + ): + type_id = self.get_unit_type_id(al_unit, True) + ally_feats[i, ind + type_id] = 1 + ind += self.unit_type_bits + elif self.unit_type_bits > 0 and self.zero_pad_unit_types: + ind += self.unit_type_bits + if self.obs_last_action: + ally_feats[i, ind:] = self.last_action[al_id] + + # Own features + ind = 0 + if self.obs_own_health: + if not self.stochastic_health: + own_feats[ind] = unit.health / unit.health_max + else: + own_feats[ind] = self._compute_health(agent_id, unit) + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(unit) + own_feats[ind] = unit.shield / max_shield + ind += 1 + + if self.stochastic_attack: + own_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + own_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.obs_own_pos: + own_feats[ind] = x / self.map_x + own_feats[ind + 1] = y / self.map_y + ind += 2 + if self.conic_fov: + own_feats[ind: ind + 2] = self.fov_directions[agent_id] + ind += 2 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + own_feats[ind + type_id] = 1 + if self.obs_starcraft: + agent_obs = np.concatenate( + ( + move_feats.flatten(), + enemy_feats.flatten(), + ally_feats.flatten(), + own_feats.flatten(), + ) + ) + + if self.obs_timestep_number: + if self.obs_starcraft: + agent_obs = np.append( + agent_obs, self._episode_steps / self.episode_limit + ) + else: + agent_obs = np.zeros(1, dtype=np.float32) + agent_obs[:] = self._episode_steps / self.episode_limit + + if self.debug: + logging.debug("Obs Agent: {}".format(agent_id).center(60, "-")) + logging.debug( + "Avail. actions {}".format( + self.get_avail_agent_actions(agent_id) + ) + ) + logging.debug("Move feats {}".format(move_feats)) + logging.debug("Enemy feats {}".format(enemy_feats)) + logging.debug("Ally feats {}".format(ally_feats)) + logging.debug("Own feats {}".format(own_feats)) + + return agent_obs + + def get_obs(self): + """Returns all agent observations in a list. + NOTE: Agents should have access only to their local observations + during decentralised execution. + """ + agents_obs = [ + self.get_obs_agent(i, fully_observable=self.fully_observable) + for i in range(self.n_agents) + ] + return agents_obs + + def get_capabilities_agent(self, agent_id): + unit = self.get_unit_by_id(agent_id) + cap_feats = np.zeros(self.get_cap_size(), dtype=np.float32) + + ind = 0 + if self.stochastic_attack: + cap_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + cap_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + cap_feats[ind + type_id] = 1 + + return cap_feats + + def get_capabilities(self): + """Returns all agent capabilities in a list.""" + agents_cap = [ + self.get_capabilities_agent(i) for i in range(self.n_agents) + ] + agents_cap = np.concatenate(agents_cap, axis=0).astype(np.float32) + return agents_cap + + def get_state(self): + """Returns the global state. + NOTE: This function should not be used during decentralised execution. + """ + if self.obs_instead_of_state: + obs_concat = np.concatenate(self.get_obs(), axis=0).astype( + np.float32 + ) + return obs_concat + + state_dict = self.get_state_dict() + + state = np.append( + state_dict["allies"].flatten(), state_dict["enemies"].flatten() + ) + if "last_action" in state_dict: + state = np.append(state, state_dict["last_action"].flatten()) + if "timestep" in state_dict: + state = np.append(state, state_dict["timestep"]) + + state = state.astype(dtype=np.float32) + + if self.debug: + logging.debug("STATE".center(60, "-")) + logging.debug("Ally state {}".format(state_dict["allies"])) + logging.debug("Enemy state {}".format(state_dict["enemies"])) + if self.state_last_action: + logging.debug("Last actions {}".format(self.last_action)) + + return state + + def get_ally_num_attributes(self): + return len(self.ally_state_attr_names) + len( + self.capability_attr_names + ) + + def get_enemy_num_attributes(self): + return len(self.enemy_state_attr_names) + + def get_state_dict(self): + """Returns the global state as a dictionary. + + - allies: numpy array containing agents and their attributes + - enemies: numpy array containing enemies and their attributes + - last_action: numpy array of previous actions for each agent + - timestep: current no. of steps divided by total no. of steps + + NOTE: This function should not be used during decentralised execution. + """ + + # number of features equals the number of attribute names + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + ally_state = np.zeros((self.n_agents, nf_al)) + enemy_state = np.zeros((self.n_enemies, nf_en)) + + center_x = self.map_x / 2 + center_y = self.map_y / 2 + + for al_id, al_unit in self.agents.items(): + if al_unit.health > 0: + x = al_unit.pos.x + y = al_unit.pos.y + max_cd = self.unit_max_cooldown(al_unit) + if not self.stochastic_health: + ally_state[al_id, 0] = ( + al_unit.health / al_unit.health_max + ) # health + else: + ally_state[al_id, 0] = self._compute_health(al_id, al_unit) + if ( + self.map_type in ["MMM", "terran_gen"] + and al_unit.unit_type == self.medivac_id + ): + ally_state[al_id, 1] = al_unit.energy / max_cd # energy + else: + ally_state[al_id, 1] = ( + al_unit.weapon_cooldown / max_cd + ) # cooldown + ally_state[al_id, 2] = ( + x - center_x + ) / self.max_distance_x # relative X + ally_state[al_id, 3] = ( + y - center_y + ) / self.max_distance_y # relative Y + + ind = 4 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_state[al_id, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + + if self.stochastic_attack: + ally_state[al_id, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + if self.stochastic_health: + ally_state[al_id, ind] = self.agent_health_levels[al_id] + ind += 1 + if self.conic_fov: + ally_state[al_id, ind: ind + 2] = self.fov_directions[ + al_id + ] + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(al_unit, True) + ally_state[al_id, type_id - self.unit_type_bits] = 1 + + for e_id, e_unit in self.enemies.items(): + if e_unit.health > 0: + x = e_unit.pos.x + y = e_unit.pos.y + + enemy_state[e_id, 0] = ( + e_unit.health / e_unit.health_max + ) # health + enemy_state[e_id, 1] = ( + x - center_x + ) / self.max_distance_x # relative X + enemy_state[e_id, 2] = ( + y - center_y + ) / self.max_distance_y # relative Y + + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_state[e_id, 3] = e_unit.shield / max_shield # shield + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(e_unit, False) + enemy_state[e_id, type_id - self.unit_type_bits] = 1 + + state = {"allies": ally_state, "enemies": enemy_state} + + if self.state_last_action: + state["last_action"] = self.last_action + if self.state_timestep_number: + state["timestep"] = self._episode_steps / self.episode_limit + + return state + + def get_obs_enemy_feats_size(self): + """Returns the dimensions of the matrix containing enemy features. + Size is n_enemies x n_features. + """ + nf_en = 4 + self.unit_type_bits + + if self.obs_all_health: + nf_en += 1 + self.shield_bits_enemy + + return self.n_enemies, nf_en + + def get_obs_ally_feats_size(self): + """Returns the dimensions of the matrix containing ally features. + Size is n_allies x n_features. + """ + nf_al = 4 + nf_cap = self.get_obs_ally_capability_size() + + if self.obs_all_health: + nf_al += 1 + self.shield_bits_ally + + if self.obs_last_action: + nf_al += self.n_actions + + return self.n_agents - 1, nf_al + nf_cap + + def get_obs_own_feats_size(self): + """ + Returns the size of the vector containing the agents' own features. + """ + own_feats = self.get_cap_size() + if self.obs_own_health and self.obs_starcraft: + own_feats += 1 + self.shield_bits_ally + if self.conic_fov and self.obs_starcraft: + own_feats += 2 + if self.obs_own_pos and self.obs_starcraft: + own_feats += 2 + return own_feats + + def get_obs_move_feats_size(self): + """Returns the size of the vector containing the agents's movement- + related features. + """ + move_feats = self.n_actions_move + if self.obs_pathing_grid: + move_feats += self.n_obs_pathing + if self.obs_terrain_height: + move_feats += self.n_obs_height + + return move_feats + + def get_obs_ally_capability_size(self): + """Returns the size of capabilities observed by teammates.""" + cap_feats = self.unit_type_bits + if self.stochastic_attack and ( + self.zero_pad_stochastic_attack or self.observe_attack_probs + ): + cap_feats += 1 + if self.stochastic_health and ( + self.observe_teammate_health or self.zero_pad_health + ): + cap_feats += 1 + + return cap_feats + + def get_cap_size(self): + """Returns the size of the own capabilities of the agent.""" + cap_feats = 0 + if self.stochastic_attack: + cap_feats += 1 + if self.stochastic_health: + cap_feats += 1 + if self.unit_type_bits > 0: + cap_feats += self.unit_type_bits + + return cap_feats + + def get_obs_size(self): + """Returns the size of the observation.""" + own_feats = self.get_obs_own_feats_size() + move_feats = self.get_obs_move_feats_size() + + n_enemies, n_enemy_feats = self.get_obs_enemy_feats_size() + n_allies, n_ally_feats = self.get_obs_ally_feats_size() + + enemy_feats = n_enemies * n_enemy_feats + ally_feats = n_allies * n_ally_feats + if self.obs_starcraft: + return ( + self.obs_timestep_number + + move_feats + + enemy_feats + + ally_feats + + own_feats + ) + else: + return 1 if self.obs_timestep_number else 0 + + def get_state_size(self): + """Returns the size of the global state.""" + if self.obs_instead_of_state: + return self.get_obs_size() * self.n_agents + + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + enemy_state = self.n_enemies * nf_en + ally_state = self.n_agents * nf_al + + size = enemy_state + ally_state + + if self.state_last_action: + size += self.n_agents * self.n_actions + if self.state_timestep_number: + size += 1 + + return size + + def get_visibility_matrix(self): + """Returns a boolean numpy array of dimensions + (n_agents, n_agents + n_enemies) indicating which units + are visible to each agent. + """ + arr = np.zeros( + (self.n_agents, self.n_agents + self.n_enemies), + dtype=np.bool, + ) + + for agent_id in range(self.n_agents): + current_agent = self.get_unit_by_id(agent_id) + if current_agent.health > 0: # it agent not dead + x = current_agent.pos.x + y = current_agent.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Enemies + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + + if dist < sight_range and e_unit.health > 0: + # visible and alive + arr[agent_id, self.n_agents + e_id] = 1 + + # The matrix for allies is filled symmetrically + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id > agent_id + ] + for _, al_id in enumerate(al_ids): + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + + if dist < sight_range and al_unit.health > 0: + # visible and alive + arr[agent_id, al_id] = arr[al_id, agent_id] = 1 + + return arr + + def get_unit_type_id(self, unit, ally): + """Returns the ID of unit type in the given scenario.""" + + if self.map_type == "protoss_gen": + if unit.unit_type in (self.stalker_id, Protoss.Stalker): + return 0 + if unit.unit_type in (self.zealot_id, Protoss.Zealot): + return 1 + if unit.unit_type in (self.colossus_id, Protoss.Colossus): + return 2 + raise AttributeError() + if self.map_type == "terran_gen": + if unit.unit_type in (self.marine_id, Terran.Marine): + return 0 + if unit.unit_type in (self.marauder_id, Terran.Marauder): + return 1 + if unit.unit_type in (self.medivac_id, Terran.Medivac): + return 2 + raise AttributeError() + + if self.map_type == "zerg_gen": + if unit.unit_type in (self.zergling_id, Zerg.Zergling): + return 0 + if unit.unit_type in (self.hydralisk_id, Zerg.Hydralisk): + return 1 + if unit.unit_type in (self.baneling_id, Zerg.Baneling): + return 2 + raise AttributeError() + + # Old stuff + if ally: # use new SC2 unit types + type_id = unit.unit_type - self._min_unit_type + + if self.map_type == "stalkers_and_zealots": + # id(Stalker) = 74, id(Zealot) = 73 + type_id = unit.unit_type - 73 + elif self.map_type == "colossi_stalkers_zealots": + # id(Stalker) = 74, id(Zealot) = 73, id(Colossus) = 4 + if unit.unit_type == 4: + type_id = 0 + elif unit.unit_type == 74: + type_id = 1 + else: + type_id = 2 + elif self.map_type == "bane": + if unit.unit_type == 9: + type_id = 0 + else: + type_id = 1 + elif self.map_type == "MMM": + if unit.unit_type == 51: + type_id = 0 + elif unit.unit_type == 48: + type_id = 1 + else: + type_id = 2 + + return type_id + + def get_avail_agent_actions(self, agent_id): + """Returns the available actions for agent_id.""" + unit = self.get_unit_by_id(agent_id) + if unit.health > 0: + # cannot choose no-op when alive + avail_actions = [0] * self.n_actions + + # stop should be allowed + avail_actions[1] = 1 + + # see if we can move + if self.can_move(unit, Direction.NORTH): + avail_actions[2] = 1 + if self.can_move(unit, Direction.SOUTH): + avail_actions[3] = 1 + if self.can_move(unit, Direction.EAST): + avail_actions[4] = 1 + if self.can_move(unit, Direction.WEST): + avail_actions[5] = 1 + + if self.conic_fov: + avail_actions[6: 6 + self.n_fov_actions] = [1] * self.n_fov_actions + + # Can attack only alive units that are alive in the shooting range + shoot_range = self.unit_shoot_range(agent_id) + + target_items = self.enemies.items() + if self.map_type in ["MMM", "terran_gen"] and unit.unit_type == self.medivac_id: + # Medivacs cannot heal themselves or other flying units + target_items = [ + (t_id, t_unit) + for (t_id, t_unit) in self.agents.items() + if t_unit.unit_type != self.medivac_id + ] + # should we only be able to target people in the cone? + for t_id, t_unit in target_items: + if t_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, t_unit.pos.x, t_unit.pos.y + ) + can_shoot = ( + dist <= shoot_range + if not self.conic_fov + else self.is_position_in_cone( + agent_id, t_unit.pos, range="shoot_range" + ) + ) + if can_shoot: + avail_actions[t_id + self.n_actions_no_attack] = 1 + + return avail_actions + + else: + # only no-op allowed + return [1] + [0] * (self.n_actions - 1) + + def get_avail_actions(self): + """Returns the available actions of all agents in a list.""" + avail_actions = [] + for agent_id in range(self.n_agents): + avail_agent = self.get_avail_agent_actions(agent_id) + avail_actions.append(avail_agent) + return avail_actions + + def close(self): + """Close StarCraft II.""" + if self.renderer is not None: + self.renderer.close() + self.renderer = None + if self._sc2_proc: + self._sc2_proc.close() + + def seed(self): + """Returns the random seed used by the environment.""" + return self._seed + + def render(self, mode="human"): + if self.renderer is None: + from smac.env.starcraft2.render import StarCraft2Renderer + + self.renderer = StarCraft2Renderer(self, mode) + assert ( + mode == self.renderer.mode + ), "mode must be consistent across render calls" + return self.renderer.render(mode) + + def _kill_units(self, unit_tags): + debug_command = [ + d_pb.DebugCommand(kill_unit=d_pb.DebugKillUnit(tag=unit_tags)) + ] + self._controller.debug(debug_command) + + def _kill_all_units(self): + """Kill all units on the map. Steps controller and so can throw + exceptions""" + units = [unit.tag for unit in self._obs.observation.raw_data.units] + self._kill_units(units) + # check the units are dead + units = len(self._obs.observation.raw_data.units) + while len(self._obs.observation.raw_data.units) > 0: + self._controller.step(2) + self._obs = self._controller.observe() + + def _create_new_team(self, team, episode_config): + # unit_names = { + # self.id_to_unit_name_map[unit.unit_type] + # for unit in self.agents.values() + # } + # It's important to set the number of agents and enemies + # because we use that to identify whether all the units have + # been created successfully + + # TODO hardcoding init location. change this later for new maps + if not self.random_start: + ally_init_pos = [sc_common.Point2D(x=8, y=16)] * self.n_agents + # Spawning location of enemy units + enemy_init_pos = [sc_common.Point2D(x=24, y=16)] * self.n_enemies + else: + ally_init_pos = [ + sc_common.Point2D( + x=self.ally_start_positions[i][0], + y=self.ally_start_positions[i][1], + ) + for i in range(self.ally_start_positions.shape[0]) + ] + enemy_init_pos = [ + sc_common.Point2D( + x=self.enemy_start_positions[i][0], + y=self.enemy_start_positions[i][1], + ) + for i in range(self.enemy_start_positions.shape[0]) + ] + for unit_id, unit in enumerate(team): + unit_type_ally = self._convert_unit_name_to_unit_type( + unit, ally=True + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_ally, + owner=1, + pos=ally_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + unit_type_enemy = self._convert_unit_name_to_unit_type( + unit, ally=False + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_enemy, + owner=2, + pos=enemy_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def _convert_unit_name_to_unit_type(self, unit_name, ally=True): + if ally: + return self.ally_unit_map[unit_name] + else: + return self.enemy_unit_map[unit_name] + + def init_units(self, team, episode_config={}): + """Initialise the units.""" + if team: + # can use any value for min unit type because + # it is hardcoded based on the version + self._init_ally_unit_types(0) + self._create_new_team(team, episode_config) + while True: + # Sometimes not all units have yet been created by SC2 + self.agents = {} + self.enemies = {} + + ally_units = [ + unit + for unit in self._obs.observation.raw_data.units + if unit.owner == 1 + ] + ally_units_sorted = sorted( + ally_units, + key=attrgetter("unit_type", "pos.x", "pos.y"), + reverse=False, + ) + + for i in range(len(ally_units_sorted)): + self.agents[i] = ally_units_sorted[i] + if self.debug: + logging.debug( + "Unit {} is {}, x = {}, y = {}".format( + len(self.agents), + self.agents[i].unit_type, + self.agents[i].pos.x, + self.agents[i].pos.y, + ) + ) + + for unit in self._obs.observation.raw_data.units: + if unit.owner == 2: + self.enemies[len(self.enemies)] = unit + if self._episode_count == 0: + self.max_reward += unit.health_max + unit.shield_max + + if self._episode_count == 0 and not team: + min_unit_type = min( + unit.unit_type for unit in self.agents.values() + ) + self._init_ally_unit_types(min_unit_type) + + all_agents_created = len(self.agents) == self.n_agents + all_enemies_created = len(self.enemies) == self.n_enemies + + self._unit_types = [ + unit.unit_type for unit in ally_units_sorted + ] + [ + unit.unit_type + for unit in self._obs.observation.raw_data.units + if unit.owner == 2 + ] + + # TODO move this to the start + if all_agents_created and all_enemies_created: # all good + return + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def get_unit_types(self): + if self._unit_types is None: + warn( + "unit types have not been initialized yet, please call" + "env.reset() to populate this and call t1286he method again." + ) + + return self._unit_types + + def update_units(self): + """Update units after an environment step. + This function assumes that self._obs is up-to-date. + """ + n_ally_alive = 0 + n_enemy_alive = 0 + + # Store previous state + self.previous_ally_units = deepcopy(self.agents) + self.previous_enemy_units = deepcopy(self.enemies) + + for al_id, al_unit in self.agents.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if al_unit.tag == unit.tag: + self.agents[al_id] = unit + updated = True + n_ally_alive += 1 + break + + if not updated: # dead + al_unit.health = 0 + + for e_id, e_unit in self.enemies.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if e_unit.tag == unit.tag: + self.enemies[e_id] = unit + updated = True + n_enemy_alive += 1 + break + + if not updated: # dead + e_unit.health = 0 + + if ( + n_ally_alive == 0 + and n_enemy_alive > 0 + or self.only_medivac_left(ally=True) + ): + return -1 # lost + if ( + n_ally_alive > 0 + and n_enemy_alive == 0 + or self.only_medivac_left(ally=False) + ): + return 1 # won + if n_ally_alive == 0 and n_enemy_alive == 0: + return 0 + + return None + + def _register_unit_mapping(self, unit_name, unit_type_id): + self.id_to_unit_name_map[unit_type_id] = unit_name + self.unit_name_to_id_map[unit_name] = unit_type_id + + def _init_ally_unit_types(self, min_unit_type): + """Initialise ally unit types. Should be called once from the + init_units function. + """ + + self._min_unit_type = min_unit_type + + if "10gen_" in self.map_name: + num_rl_units = 9 + self._min_unit_type = ( + len(self._controller.data().units) - num_rl_units + ) + + self.baneling_id = self._min_unit_type + self.colossus_id = self._min_unit_type + 1 + self.hydralisk_id = self._min_unit_type + 2 + self.marauder_id = self._min_unit_type + 3 + self.marine_id = self._min_unit_type + 4 + self.medivac_id = self._min_unit_type + 5 + self.stalker_id = self._min_unit_type + 6 + self.zealot_id = self._min_unit_type + 7 + self.zergling_id = self._min_unit_type + 8 + + self.ally_unit_map = { + "baneling": self.baneling_id, + "colossus": self.colossus_id, + "hydralisk": self.hydralisk_id, + "marauder": self.marauder_id, + "marine": self.marine_id, + "medivac": self.medivac_id, + "stalker": self.stalker_id, + "zealot": self.zealot_id, + "zergling": self.zergling_id, + } + self.enemy_unit_map = { + "baneling": Zerg.Baneling, + "colossus": Protoss.Colossus, + "hydralisk": Zerg.Hydralisk, + "marauder": Terran.Marauder, + "marine": Terran.Marine, + "medivac": Terran.Medivac, + "stalker": Protoss.Stalker, + "zealot": Protoss.Zealot, + "zergling": Zerg.Zergling, + } + + else: + if self.map_type == "marines": + self.marine_id = min_unit_type + self._register_unit_mapping("marine", min_unit_type) + elif self.map_type == "stalkers_and_zealots": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + self.zealot_id = min_unit_type + 1 + self._register_unit_mapping("zealot", min_unit_type + 1) + elif self.map_type == "colossi_stalkers_zealots": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + self.stalker_id = min_unit_type + 1 + self._register_unit_mapping("stalker", min_unit_type + 1) + self.zealot_id = min_unit_type + 2 + self._register_unit_mapping("zealot", min_unit_type + 2) + elif self.map_type == "MMM": + self.marauder_id = min_unit_type + self._register_unit_mapping("marauder", min_unit_type) + self.marine_id = min_unit_type + 1 + self._register_unit_mapping("marine", min_unit_type + 1) + self.medivac_id = min_unit_type + 2 + self._register_unit_mapping("medivac", min_unit_type + 2) + elif self.map_type == "zealots": + self.zealot_id = min_unit_type + self._register_unit_mapping("zealot", min_unit_type) + elif self.map_type == "hydralisks": + self.hydralisk_id = min_unit_type + self._register_unit_mapping("hydralisk", min_unit_type) + elif self.map_type == "stalkers": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + elif self.map_type == "colossus": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + elif self.map_type == "bane": + self.baneling_id = min_unit_type + self._register_unit_mapping("baneling", min_unit_type) + self.zergling_id = min_unit_type + 1 + self._register_unit_mapping("zergling", min_unit_type + 1) + + def only_medivac_left(self, ally): + """Check if only Medivac units are left.""" + if self.map_type != "MMM" and self.map_type != "terran_gen": + return False + + if ally: + units_alive = [ + a + for a in self.agents.values() + if (a.health > 0 and a.unit_type != self.medivac_id) + ] + if len(units_alive) == 0: + return True + return False + else: + units_alive = [ + a + for a in self.enemies.values() + if (a.health > 0 and a.unit_type != Terran.Medivac) + ] + if len(units_alive) == 0: + return True + return False + + def get_unit_by_id(self, a_id): + """Get unit by ID.""" + return self.agents[a_id] + + def get_stats(self): + stats = { + "battles_won": self.battles_won, + "battles_game": self.battles_game, + "battles_draw": self.timeouts, + "win_rate": self.battles_won / self.battles_game, + "timeouts": self.timeouts, + "restarts": self.force_restarts, + } + return stats + + def get_env_info(self): + env_info = super().get_env_info() + env_info["agent_features"] = ( + self.ally_state_attr_names + self.capability_attr_names + ) + env_info["enemy_features"] = self.enemy_state_attr_names + return env_info diff --git a/src/envs/smac_v2/official/starcraft2_hxt.py b/src/envs/smac_v2/official/starcraft2_hxt.py new file mode 100644 index 0000000..6bc6423 --- /dev/null +++ b/src/envs/smac_v2/official/starcraft2_hxt.py @@ -0,0 +1,2303 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from envs.multiagentenv import MultiAgentEnv +from .maps import get_map_params + +import atexit +from warnings import warn +from operator import attrgetter +from copy import deepcopy +import numpy as np +import enum +import math +from absl import logging +from pysc2.lib.units import Neutral, Protoss, Terran, Zerg + +from pysc2 import maps +from pysc2 import run_configs +from pysc2.lib import protocol + +from s2clientprotocol import common_pb2 as sc_common +from s2clientprotocol import sc2api_pb2 as sc_pb +from s2clientprotocol import raw_pb2 as r_pb +from s2clientprotocol import debug_pb2 as d_pb + +races = { + "R": sc_common.Random, + "P": sc_common.Protoss, + "T": sc_common.Terran, + "Z": sc_common.Zerg, +} + +difficulties = { + "1": sc_pb.VeryEasy, + "2": sc_pb.Easy, + "3": sc_pb.Medium, + "4": sc_pb.MediumHard, + "5": sc_pb.Hard, + "6": sc_pb.Harder, + "7": sc_pb.VeryHard, + "8": sc_pb.CheatVision, + "9": sc_pb.CheatMoney, + "A": sc_pb.CheatInsane, +} + +actions = { + "move": 16, # target: PointOrUnit + "attack": 23, # target: PointOrUnit + "stop": 4, # target: None + "heal": 386, # Unit +} + + +class Direction(enum.IntEnum): + NORTH = 0 + SOUTH = 1 + EAST = 2 + WEST = 3 + + +EPS = 1e-7 + + +class StarCraft2Env(MultiAgentEnv): + """The StarCraft II environment for decentralised multi-agent + micromanagement scenarios. + """ + + def __init__( + self, + map_name="8m", + step_mul=8, + move_amount=2, + difficulty="7", + game_version=None, + seed=None, + continuing_episode=False, + obs_all_health=True, + obs_own_health=True, + obs_last_action=False, + obs_pathing_grid=False, + obs_terrain_height=False, + obs_instead_of_state=False, + obs_timestep_number=False, + obs_own_pos=False, + obs_starcraft=True, + conic_fov=False, + num_fov_actions=4, + state_last_action=True, + state_timestep_number=False, + reward_sparse=False, + reward_only_positive=True, + reward_death_value=10, + reward_win=200, + reward_defeat=0, + reward_negative_scale=0.5, + reward_scale=True, + reward_scale_rate=20, + kill_unit_step_mul=2, + fully_observable=False, + capability_config={}, + replay_dir="", + replay_prefix="", + window_size_x=1920, + window_size_y=1200, + heuristic_ai=False, + heuristic_rest=False, + debug=False, + ): + """ + Create a StarCraftC2Env environment. + + Parameters + ---------- + map_name : str, optional + The name of the SC2 map to play (default is "8m"). The full list + can be found by running bin/map_list. + step_mul : int, optional + How many game steps per agent step (default is 8). None + indicates to use the default map step_mul. + move_amount : float, optional + How far away units are ordered to move per step (default is 2). + difficulty : str, optional + The difficulty of built-in computer AI bot (default is "7"). + game_version : str, optional + StarCraft II game version (default is None). None indicates the + latest version. + seed : int, optional + Random seed used during game initialisation. This allows to + continuing_episode : bool, optional + Whether to consider episodes continuing or finished after time + limit is reached (default is False). + obs_all_health : bool, optional + Agents receive the health of all units (in the sight range) as part + of observations (default is True). + obs_own_health : bool, optional + Agents receive their own health as a part of observations (default + is False). This flag is ignored when obs_all_health == True. + obs_last_action : bool, optional + Agents receive the last actions of all units (in the sight range) + as part of observations (default is False). + obs_pathing_grid : bool, optional + Whether observations include pathing values surrounding the agent + (default is False). + obs_terrain_height : bool, optional + Whether observations include terrain height values surrounding the + agent (default is False). + obs_instead_of_state : bool, optional + Use combination of all agents' observations as the global state + (default is False). + obs_timestep_number : bool, optional + Whether observations include the current timestep of the episode + (default is False). + state_last_action : bool, optional + Include the last actions of all agents as part of the global state + (default is True). + state_timestep_number : bool, optional + Whether the state include the current timestep of the episode + (default is False). + reward_sparse : bool, optional + Receive 1/-1 reward for winning/loosing an episode (default is + False). Whe rest of reward parameters are ignored if True. + reward_only_positive : bool, optional + Reward is always positive (default is True). + reward_death_value : float, optional + The amount of reward received for killing an enemy unit (default + is 10). This is also the negative penalty for having an allied unit + killed if reward_only_positive == False. + reward_win : float, optional + The reward for winning in an episode (default is 200). + reward_defeat : float, optional + The reward for loosing in an episode (default is 0). This value + should be nonpositive. + reward_negative_scale : float, optional + Scaling factor for negative rewards (default is 0.5). This + parameter is ignored when reward_only_positive == True. + reward_scale : bool, optional + Whether or not to scale the reward (default is True). + reward_scale_rate : float, optional + Reward scale rate (default is 20). When reward_scale == True, the + reward received by the agents is divided by (max_reward / + reward_scale_rate), where max_reward is the maximum possible + reward per episode without considering the shield regeneration + of Protoss units. + replay_dir : str, optional + The directory to save replays (default is None). If None, the + replay will be saved in Replays directory where StarCraft II is + installed. + replay_prefix : str, optional + The prefix of the replay to be saved (default is None). If None, + the name of the map will be used. + window_size_x : int, optional + The length of StarCraft II window size (default is 1920). + window_size_y: int, optional + The height of StarCraft II window size (default is 1200). + heuristic_ai: bool, optional + Whether or not to use a non-learning heuristic AI (default False). + heuristic_rest: bool, optional + At any moment, restrict the actions of the heuristic AI to be + chosen from actions available to RL agents (default is False). + Ignored if heuristic_ai == False. + debug: bool, optional + Log messages about observations, state, actions and rewards for + debugging purposes (default is False). + """ + # Map arguments + self.map_name = map_name + map_params = get_map_params(self.map_name) + self.map_params = map_params + self.episode_limit = map_params["limit"] + self._move_amount = move_amount + self._step_mul = step_mul + self._kill_unit_step_mul = kill_unit_step_mul + self.difficulty = difficulty + + # Observations and state + self.obs_own_health = obs_own_health + self.obs_all_health = obs_all_health + self.obs_instead_of_state = obs_instead_of_state + self.obs_last_action = obs_last_action + self.obs_pathing_grid = obs_pathing_grid + self.obs_terrain_height = obs_terrain_height + self.obs_timestep_number = obs_timestep_number + self.obs_starcraft = obs_starcraft + self.state_last_action = state_last_action + self.state_timestep_number = state_timestep_number + if self.obs_all_health: + self.obs_own_health = True + self.n_obs_pathing = 8 + self.n_obs_height = 9 + + # Rewards args + self.reward_sparse = reward_sparse + self.reward_only_positive = reward_only_positive + self.reward_negative_scale = reward_negative_scale + self.reward_death_value = reward_death_value + self.reward_win = reward_win + self.reward_defeat = reward_defeat + self.reward_scale = reward_scale + self.reward_scale_rate = reward_scale_rate + + # Meta MARL + self.capability_config = capability_config + self.fully_observable = fully_observable + self.stochastic_attack = "attack" in self.capability_config + self.stochastic_health = "health" in self.capability_config + self.replace_teammates = "team_gen" in self.capability_config + self.obs_own_pos = obs_own_pos + self.mask_enemies = "enemy_mask" in self.capability_config + if self.stochastic_attack: + self.zero_pad_stochastic_attack = not self.capability_config[ + "attack" + ]["observe"] + self.observe_attack_probs = self.capability_config["attack"][ + "observe" + ] + if self.stochastic_health: + self.zero_pad_health = not self.capability_config["health"][ + "observe" + ] + self.observe_teammate_health = self.capability_config["health"][ + "observe" + ] + if self.replace_teammates: + self.zero_pad_unit_types = not self.capability_config["team_gen"][ + "observe" + ] + self.observe_teammate_types = self.capability_config["team_gen"][ + "observe" + ] + self.n_agents = ( + map_params["n_agents"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.n_enemies = ( + map_params["n_enemies"] + if not self.replace_teammates + else self.capability_config["team_gen"]["n_units"] + ) + self.random_start = "start_positions" in self.capability_config + self.conic_fov = conic_fov + if self.conic_fov: + num_fov_actions = 4 + self.n_fov_actions = num_fov_actions if self.conic_fov else 0 + self.conic_fov_angle = ( + (2 * np.pi) / self.n_fov_actions if self.conic_fov else 0 + ) + # Other + self.game_version = game_version + self.continuing_episode = continuing_episode + self._seed = seed + self.heuristic_ai = heuristic_ai + self.heuristic_rest = heuristic_rest + self.debug = debug + self.window_size = (window_size_x, window_size_y) + self.replay_dir = replay_dir + self.replay_prefix = replay_prefix + + # Actions + self.n_actions_move = 4 + + self.n_actions_no_attack = self.n_actions_move + 2 + self.n_actions = self.n_actions_no_attack + self.n_enemies + + # Map info + self._agent_race = map_params["a_race"] + self._bot_race = map_params["b_race"] + self.shield_bits_ally = 1 if self._agent_race == "P" else 0 + self.shield_bits_enemy = 1 if self._bot_race == "P" else 0 + # NOTE: The map_type, which is used to initialise the unit + # type ids, the unit_type_bits and the races, are still properties of the + # map. This means even the 10gen_{race} maps are limited to the + # unit types statically defined in the unit type id assignment. + # Lifting this restriction shouldn't be too much work, I've just + # not done it. + self.unit_type_bits = map_params["unit_type_bits"] + self.map_type = map_params["map_type"] + self._unit_types = None + + self.max_reward = ( + self.n_enemies * self.reward_death_value + self.reward_win + ) + + # create lists containing the names of attributes returned in states + self.ally_state_attr_names = [ + "health", + "energy/cooldown", + "rel_x", + "rel_y", + ] + self.enemy_state_attr_names = ["health", "rel_x", "rel_y"] + + if self.shield_bits_ally > 0: + self.ally_state_attr_names += ["shield"] + if self.shield_bits_enemy > 0: + self.enemy_state_attr_names += ["shield"] + if self.conic_fov: + self.ally_state_attr_names += ["fov_x", "fov_y"] + + self.capability_attr_names = [] + if "attack" in self.capability_config: + self.capability_attr_names += ["attack_probability"] + if "health" in self.capability_config: + self.capability_attr_names += ["total_health"] + if self.unit_type_bits > 0: + bit_attr_names = [ + "type_{}".format(bit) for bit in range(self.unit_type_bits) + ] + self.capability_attr_names += bit_attr_names + self.enemy_state_attr_names += bit_attr_names + + self.agents = {} + self.enemies = {} + self.unit_name_to_id_map = {} + self.id_to_unit_name_map = {} + self._episode_count = 0 + self._episode_steps = 0 + self._total_steps = 0 + self._obs = None + self.battles_won = 0 + self.battles_game = 0 + self.timeouts = 0 + self.force_restarts = 0 + self.last_stats = None + self.agent_attack_probabilities = np.zeros(self.n_agents) + self.agent_health_levels = np.zeros(self.n_agents) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.canonical_fov_directions = np.array( + [ + ( + np.cos(2 * np.pi * (i / self.n_fov_actions)), + np.sin(2 * np.pi * (i / self.n_fov_actions)), + ) + for i in range(self.n_fov_actions) + ] + ) + self.new_unit_positions = np.zeros((self.n_agents, 2)) + self.previous_ally_units = None + self.previous_enemy_units = None + self.last_action = np.zeros((self.n_agents, self.n_actions)) + self.init_positions = np.zeros((self.n_agents, 2)) + self._min_unit_type = 0 + self.marine_id = self.marauder_id = self.medivac_id = 0 + self.hydralisk_id = self.zergling_id = self.baneling_id = 0 + self.stalker_id = self.colossus_id = self.zealot_id = 0 + self.max_distance_x = 0 + self.max_distance_y = 0 + self.map_x = 0 + self.map_y = 0 + self.reward = 0 + self.renderer = None + self.terrain_height = None + self.pathing_grid = None + self._run_config = None + self._sc2_proc = None + self._controller = None + # Try to avoid leaking SC2 processes on shutdown + atexit.register(lambda: self.close()) + + def _only_one_meta_marl_flag_on(self): + """Function that checks that either all the meta marl flags are off, + or at most one has been enabled.""" + if self.stochastic_attack: + return not self.stochastic_health and not self.replace_teammates + else: + return not self.replace_teammates or not self.stochastic_health + + def _launch(self): + """Launch the StarCraft II game.""" + self._run_config = run_configs.get(version=self.game_version) + self.version = self._run_config.version + _map = maps.get(self.map_name) + + # Setting up the interface + interface_options = sc_pb.InterfaceOptions(raw=True, score=False) + self._sc2_proc = self._run_config.start( + window_size=self.window_size, want_rgb=False + ) + self._controller = self._sc2_proc.controller + + # Request to create the game + create = sc_pb.RequestCreateGame( + local_map=sc_pb.LocalMap( + map_path=_map.path, + map_data=self._run_config.map_data(_map.path), + ), + realtime=False, + random_seed=self._seed, + ) + create.player_setup.add(type=sc_pb.Participant) + create.player_setup.add( + type=sc_pb.Computer, + race=races[self._bot_race], + difficulty=difficulties[self.difficulty], + ) + self._controller.create_game(create) + + join = sc_pb.RequestJoinGame( + race=races[self._agent_race], options=interface_options + ) + self._controller.join_game(join) + + game_info = self._controller.game_info() + map_info = game_info.start_raw + self.map_play_area_min = map_info.playable_area.p0 + self.map_play_area_max = map_info.playable_area.p1 + self.max_distance_x = ( + self.map_play_area_max.x - self.map_play_area_min.x + ) + self.max_distance_y = ( + self.map_play_area_max.y - self.map_play_area_min.y + ) + self.map_x = map_info.map_size.x + self.map_y = map_info.map_size.y + + if map_info.pathing_grid.bits_per_pixel == 1: + vals = np.array(list(map_info.pathing_grid.data)).reshape( + self.map_x, int(self.map_y / 8) + ) + self.pathing_grid = np.transpose( + np.array( + [ + [(b >> i) & 1 for b in row for i in range(7, -1, -1)] + for row in vals + ], + dtype=np.bool, + ) + ) + else: + self.pathing_grid = np.invert( + np.flip( + np.transpose( + np.array( + list(map_info.pathing_grid.data), dtype=np.bool + ).reshape(self.map_x, self.map_y) + ), + axis=1, + ) + ) + + self.terrain_height = ( + np.flip( + np.transpose( + np.array(list(map_info.terrain_height.data)).reshape( + self.map_x, self.map_y + ) + ), + 1, + ) + / 255 + ) + + def reset(self, episode_config={}): + """Reset the environment. Required after each full episode. + Returns initial observations and states. + """ + self._episode_steps = 0 + self.episode_config = episode_config + if self._episode_count == 0: + # Launch StarCraft II + self._launch() + else: + self._restart() + + # Information kept for counting the reward + self.agent_attack_probabilities = episode_config.get("attack", {}).get( + "item", None + ) + self.agent_health_levels = episode_config.get("health", {}).get( + "item", None + ) + self.enemy_mask = episode_config.get("enemy_mask", {}).get( + "item", None + ) + self.ally_start_positions = episode_config.get( + "ally_start_positions", {} + ).get("item", None) + self.enemy_start_positions = episode_config.get( + "enemy_start_positions", {} + ).get("item", None) + self.mask_enemies = self.enemy_mask is not None + team = episode_config.get("team_gen", {}).get("item", None) + self.death_tracker_ally = np.zeros(self.n_agents) + self.death_tracker_enemy = np.zeros(self.n_enemies) + self.fov_directions = np.zeros((self.n_agents, 2)) + self.fov_directions[:, 0] = 1.0 + self.previous_ally_units = None + self.previous_enemy_units = None + self.win_counted = False + self.defeat_counted = False + if self.debug: + logging.debug( + "Attack Probabilities: {}".format(self.agent_attack_probabilities) + ) + logging.debug("Health Levels: {}".format(self.agent_health_levels)) + self.last_action = np.zeros((self.n_agents, self.n_actions)) + + if self.heuristic_ai: + self.heuristic_targets = [None] * self.n_agents + + try: + self._obs = self._controller.observe() + self.init_units(team, episode_config=episode_config) + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + if self.debug: + logging.debug( + "Started Episode {}".format(self._episode_count).center( + 60, "*" + ) + ) + return self.get_obs(), self.get_state() + + def _restart(self): + """Restart the environment by killing all units on the map. + There is a trigger in the SC2Map file, which restarts the + episode when there are no units left. + """ + try: + self._kill_all_units() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + + def full_restart(self): + """Full restart. Closes the SC2 process and launches a new one.""" + self._sc2_proc.close() + self._launch() + self.force_restarts += 1 + + def _kill_units_below_health_level(self): + units_to_kill = [] + for al_id, al_unit in self.agents.items(): + if ( + al_unit.health / al_unit.health_max + < self.agent_health_levels[al_id] + ) and not self.death_tracker_ally[al_id]: + units_to_kill.append(al_unit.tag) + self._kill_units(units_to_kill) + + def step(self, actions): + """A single environment step. Returns reward, terminated, info.""" + actions_int = [int(a) for a in actions] + + self.last_action = np.eye(self.n_actions)[np.array(actions_int)] + + # Collect individual actions + sc_actions = [] + if self.debug: + logging.debug("Actions".center(60, "-")) + + for a_id, action in enumerate(actions_int): + if not self.heuristic_ai: + sc_action = self.get_agent_action(a_id, action) + else: + sc_action, action_num = self.get_agent_action_heuristic( + a_id, action + ) + actions[a_id] = action_num + if sc_action: + sc_actions.append(sc_action) + # Send action request + req_actions = sc_pb.RequestAction(actions=sc_actions) + + try: + if self.conic_fov: + self.render_fovs() + self._controller.actions(req_actions) + # Make step in SC2, i.e. apply actions + if not self.stochastic_health: + self._controller.step(self._step_mul) + else: + self._controller.step( + self._step_mul - self._kill_unit_step_mul + ) + self._kill_units_below_health_level() + self._controller.step(self._kill_unit_step_mul) + # Observe here so that we know if the episode is over. + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + return 0, True, {} + + self._total_steps += 1 + self._episode_steps += 1 + + # Update units + game_end_code = self.update_units() + + terminated = False + reward = self.reward_battle() + info = {"battle_won": False} + + # count units that are still alive + dead_allies, dead_enemies = 0, 0 + for _al_id, al_unit in self.agents.items(): + if al_unit.health == 0: + dead_allies += 1 + for _e_id, e_unit in self.enemies.items(): + if e_unit.health == 0: + dead_enemies += 1 + + info["dead_allies"] = dead_allies + info["dead_enemies"] = dead_enemies + + if game_end_code is not None: + # Battle is over + terminated = True + self.battles_game += 1 + if game_end_code == 1 and not self.win_counted: + self.battles_won += 1 + self.win_counted = True + info["battle_won"] = True + if not self.reward_sparse: + reward += self.reward_win + else: + reward = 1 + elif game_end_code == -1 and not self.defeat_counted: + self.defeat_counted = True + if not self.reward_sparse: + reward += self.reward_defeat + else: + reward = -1 + + elif self._episode_steps >= self.episode_limit: + # Episode limit reached + terminated = True + if self.continuing_episode: + info["episode_limit"] = True + self.battles_game += 1 + self.timeouts += 1 + + if self.debug: + logging.debug("Reward = {}".format(reward).center(60, "-")) + + if terminated: + self._episode_count += 1 + + if self.reward_scale: + reward /= self.max_reward / self.reward_scale_rate + + self.reward = reward + + return reward, terminated, info + + def get_agent_action(self, a_id, action): + """Construct the action for agent a_id.""" + avail_actions = self.get_avail_agent_actions(a_id) + assert ( + avail_actions[action] == 1 + ), "Agent {} cannot perform action {}".format(a_id, action) + + unit = self.get_unit_by_id(a_id) + tag = unit.tag + x = unit.pos.x + y = unit.pos.y + + if action == 0: + # no-op (valid only when dead) + assert unit.health == 0, "No-op only available for dead agents." + if self.debug: + logging.debug("Agent {}: Dead".format(a_id)) + return None + elif action == 1: + # stop + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["stop"], + unit_tags=[tag], + queue_command=False, + ) + if self.debug: + logging.debug("Agent {}: Stop".format(a_id)) + + elif action == 2: + # move north + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y + self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y + self._move_amount] + ) + if self.conic_fov: + self.fov_directions[a_id] = self.canonical_fov_directions[1] + if self.debug: + logging.debug("Agent {}: Move North".format(a_id)) + + elif action == 3: + # move south + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x, y=y - self._move_amount + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x, y - self._move_amount] + ) + if self.conic_fov: + self.fov_directions[a_id] = self.canonical_fov_directions[3] + if self.debug: + logging.debug("Agent {}: Move South".format(a_id)) + + elif action == 4: + # move east + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x + self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x + self._move_amount, y] + ) + if self.conic_fov: + self.fov_directions[a_id] = self.canonical_fov_directions[0] + if self.debug: + logging.debug("Agent {}: Move East".format(a_id)) + + elif action == 5: + # move west + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=sc_common.Point2D( + x=x - self._move_amount, y=y + ), + unit_tags=[tag], + queue_command=False, + ) + self.new_unit_positions[a_id] = np.array( + [x - self._move_amount, y] + ) + if self.conic_fov: + self.fov_directions[a_id] = self.canonical_fov_directions[2] + if self.debug: + logging.debug("Agent {}: Move West".format(a_id)) + else: + # attack/heal units that are in range + target_id = action - self.n_actions_no_attack + if ( + self.map_type in ["MMM", "terran_gen"] + and unit.unit_type == self.medivac_id + ): + target_unit = self.agents[target_id] + action_name = "heal" + else: + target_unit = self.enemies[target_id] + action_name = "attack" + + if self.stochastic_attack: + p = np.random.default_rng().uniform() + if p > self.agent_attack_probabilities[a_id]: + if self.debug: + logging.debug( + "Agent {} {}s {}, but fails".format(a_id, action_name, target_id) + ) + return None + action_id = actions[action_name] + target_tag = target_unit.tag + + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + if self.debug: + logging.debug( + "Agent {} {}s unit # {}".format( + a_id, action_name, target_id + ) + ) + if cmd: + sc_action = sc_pb.Action( + action_raw=r_pb.ActionRaw(unit_command=cmd) + ) + return sc_action + return None + + def get_agent_action_heuristic(self, a_id, action): + unit = self.get_unit_by_id(a_id) + tag = unit.tag + + target = self.heuristic_targets[a_id] + if unit.unit_type == self.medivac_id: + if ( + target is None + or self.agents[target].health == 0 + or self.agents[target].health == self.agents[target].health_max + ): + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for al_id, al_unit in self.agents.items(): + if al_unit.unit_type == self.medivac_id: + continue + if ( + al_unit.health != 0 + and al_unit.health != al_unit.health_max + ): + dist = self.distance( + unit.pos.x, + unit.pos.y, + al_unit.pos.x, + al_unit.pos.y, + ) + if dist < min_dist: + min_dist = dist + min_id = al_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["heal"] + target_tag = self.agents[self.heuristic_targets[a_id]].tag + else: + if target is None or self.enemies[target].health == 0: + min_dist = math.hypot(self.max_distance_x, self.max_distance_y) + min_id = -1 + for e_id, e_unit in self.enemies.items(): + if ( + unit.unit_type == self.marauder_id + and e_unit.unit_type == self.medivac_id + ): + continue + if e_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, e_unit.pos.x, e_unit.pos.y + ) + if dist < min_dist: + min_dist = dist + min_id = e_id + self.heuristic_targets[a_id] = min_id + if min_id == -1: + self.heuristic_targets[a_id] = None + return None, 0 + action_id = actions["attack"] + target_tag = self.enemies[self.heuristic_targets[a_id]].tag + + action_num = self.heuristic_targets[a_id] + self.n_actions_no_attack + + # Check if the action is available + if ( + self.heuristic_rest + and self.get_avail_agent_actions(a_id)[action_num] == 0 + ): + + # Move towards the target rather than attacking/healing + if unit.unit_type == self.medivac_id: + target_unit = self.agents[self.heuristic_targets[a_id]] + else: + target_unit = self.enemies[self.heuristic_targets[a_id]] + + delta_x = target_unit.pos.x - unit.pos.x + delta_y = target_unit.pos.y - unit.pos.y + + if abs(delta_x) > abs(delta_y): # east or west + if delta_x > 0: # east + target_pos = sc_common.Point2D( + x=unit.pos.x + self._move_amount, y=unit.pos.y + ) + action_num = 4 + else: # west + target_pos = sc_common.Point2D( + x=unit.pos.x - self._move_amount, y=unit.pos.y + ) + action_num = 5 + else: # north or south + if delta_y > 0: # north + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y + self._move_amount + ) + action_num = 2 + else: # south + target_pos = sc_common.Point2D( + x=unit.pos.x, y=unit.pos.y - self._move_amount + ) + action_num = 3 + + cmd = r_pb.ActionRawUnitCommand( + ability_id=actions["move"], + target_world_space_pos=target_pos, + unit_tags=[tag], + queue_command=False, + ) + else: + # Attack/heal the target + cmd = r_pb.ActionRawUnitCommand( + ability_id=action_id, + target_unit_tag=target_tag, + unit_tags=[tag], + queue_command=False, + ) + + sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd)) + return sc_action, action_num + + def reward_battle(self): + """Reward function when self.reward_spare==False. + Returns accumulative hit/shield point damage dealt to the enemy + + reward_death_value per enemy unit killed, and, in case + self.reward_only_positive == False, - (damage dealt to ally units + + reward_death_value per ally unit killed) * self.reward_negative_scale + """ + assert ( + not self.stochastic_health or self.reward_only_positive + ), "Different Health Levels are currently only compatible with positive rewards" + if self.reward_sparse: + return 0 + + reward = 0 + delta_deaths = 0 + delta_ally = 0 + delta_enemy = 0 + + neg_scale = self.reward_negative_scale + + # update deaths + for al_id, al_unit in self.agents.items(): + if not self.death_tracker_ally[al_id]: + # did not die so far + prev_health = ( + self.previous_ally_units[al_id].health + + self.previous_ally_units[al_id].shield + ) + if al_unit.health == 0: + # just died + self.death_tracker_ally[al_id] = 1 + if not self.reward_only_positive: + delta_deaths -= self.reward_death_value * neg_scale + delta_ally += prev_health * neg_scale + else: + # still alive + delta_ally += neg_scale * ( + prev_health - al_unit.health - al_unit.shield + ) + + for e_id, e_unit in self.enemies.items(): + if not self.death_tracker_enemy[e_id]: + prev_health = ( + self.previous_enemy_units[e_id].health + + self.previous_enemy_units[e_id].shield + ) + if e_unit.health == 0: + self.death_tracker_enemy[e_id] = 1 + delta_deaths += self.reward_death_value + delta_enemy += prev_health + else: + delta_enemy += prev_health - e_unit.health - e_unit.shield + + if self.reward_only_positive: + reward = max(delta_enemy + delta_deaths, 0) # shield regeneration + else: + reward = delta_enemy + delta_deaths - delta_ally + + return reward + + def get_total_actions(self): + """Returns the total number of actions an agent could ever take.""" + return self.n_actions + + @staticmethod + def distance(x1, y1, x2, y2): + """Distance between two points.""" + return math.hypot(x2 - x1, y2 - y1) + + def unit_shoot_range(self, agent_id): + """Returns the shooting range for an agent.""" + return 6 + + def unit_sight_range(self, agent_id): + """Returns the sight range for an agent.""" + return 9 + + def unit_max_cooldown(self, unit): + """Returns the maximal cooldown for a unit.""" + switcher = { + self.marine_id: 15, + self.marauder_id: 25, + self.medivac_id: 200, # max energy + self.stalker_id: 35, + self.zealot_id: 22, + self.colossus_id: 24, + self.hydralisk_id: 10, + self.zergling_id: 11, + self.baneling_id: 1, + } + return switcher.get(unit.unit_type, 15) + + def save_replay(self): + """Save a replay.""" + prefix = self.replay_prefix or self.map_name + replay_dir = self.replay_dir or "" + replay_path = self._run_config.save_replay( + self._controller.save_replay(), + replay_dir=replay_dir, + prefix=prefix, + ) + logging.info("Replay saved at: %s" % replay_path) + + def unit_max_shield(self, unit): + """Returns maximal shield for a given unit.""" + if unit.unit_type == 74 or unit.unit_type == self.stalker_id: + return 80 # Protoss's Stalker + elif unit.unit_type == 73 or unit.unit_type == self.zealot_id: + return 50 # Protoss's Zealot + elif unit.unit_type == 4 or unit.unit_type == self.colossus_id: + return 150 # Protoss's Colossus + else: + raise Exception("Maximum shield not recognised") + + def can_move(self, unit, direction): + """Whether a unit can move in a given direction.""" + m = self._move_amount / 2 + + if direction == Direction.NORTH: + x, y = int(unit.pos.x), int(unit.pos.y + m) + elif direction == Direction.SOUTH: + x, y = int(unit.pos.x), int(unit.pos.y - m) + elif direction == Direction.EAST: + x, y = int(unit.pos.x + m), int(unit.pos.y) + else: + x, y = int(unit.pos.x - m), int(unit.pos.y) + + if self.check_bounds(x, y) and self.pathing_grid[x, y]: + return True + + return False + + def get_surrounding_points(self, unit, include_self=False): + """Returns the surrounding points of the unit in 8 directions.""" + x = int(unit.pos.x) + y = int(unit.pos.y) + + ma = self._move_amount + + points = [ + (x, y + 2 * ma), + (x, y - 2 * ma), + (x + 2 * ma, y), + (x - 2 * ma, y), + (x + ma, y + ma), + (x - ma, y - ma), + (x + ma, y - ma), + (x - ma, y + ma), + ] + + if include_self: + points.append((x, y)) + + return points + + def check_bounds(self, x, y): + """Whether a point is within the map bounds.""" + return 0 <= x < self.map_x and 0 <= y < self.map_y + + def get_surrounding_pathing(self, unit): + """Returns pathing values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=False) + vals = [ + self.pathing_grid[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def get_surrounding_height(self, unit): + """Returns height values of the grid surrounding the given unit.""" + points = self.get_surrounding_points(unit, include_self=True) + vals = [ + self.terrain_height[x, y] if self.check_bounds(x, y) else 1 + for x, y in points + ] + return vals + + def _compute_health(self, agent_id, unit): + """Each agent has a health bar with max health + `health_max` and current health `health`. We set a level + `health_level` between `0` and `1` where the agent dies if its + proportional health (`health / health_max`) is below that level. + This function rescales health to take into account this death level. + + In the proportional health scale we have something that looks like this: + + ------------------------------------------------------------- + 0 1 + ^ health_level ^ proportional_health + And so we compute + (proportional_health - health_level) / (1 - health_level) + """ + proportional_health = unit.health / unit.health_max + health_level = self.agent_health_levels[agent_id] + return (1.0 / (1 - health_level)) * ( + proportional_health - health_level + ) + + def render_fovs(self): + lines_to_render = [] + for agent_id in range(self.n_agents): + if not self.death_tracker_ally[agent_id]: + lines_to_render.extend(self.agent_cone(agent_id)) + debug_command = d_pb.DebugCommand( + draw=d_pb.DebugDraw(lines=lines_to_render) + ) + self._controller.debug(debug_command) + + def agent_cone(self, agent_id): + fov_direction = self.fov_directions[agent_id] + c, s = np.cos(self.conic_fov_angle / 2), np.sin( + self.conic_fov_angle / 2 + ) + sight_range = self.unit_sight_range(agent_id) + rot = np.array([[c, -s], [s, c]]) # Contra Rotate + neg_rot = np.array([[c, s], [-s, c]]) # Rotate Clockwise + start_pos = self.new_unit_positions[agent_id] + init_pos = sc_common.Point( + x=start_pos[0], + y=start_pos[1], + z=self.get_unit_by_id(agent_id).pos.z, + ) + upper_cone_end = start_pos + (rot @ fov_direction) * sight_range + lower_cone_end = start_pos + (neg_rot @ fov_direction) * sight_range + lines = [ + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=upper_cone_end[0], + y=upper_cone_end[1], + z=init_pos.z, + ), + ) + ), + d_pb.DebugLine( + line=d_pb.Line( + p0=init_pos, + p1=sc_common.Point( + x=lower_cone_end[0], + y=lower_cone_end[1], + z=init_pos.z, + ), + ) + ), + ] + return lines + + def is_position_in_cone(self, agent_id, pos, range="sight_range"): + ally_pos = self.get_unit_by_id(agent_id).pos + distance = self.distance(ally_pos.x, ally_pos.y, pos.x, pos.y) + # position is in this agent's cone if it is not outside the sight + # range and has the correct angle + if range == "sight_range": + unit_range = self.unit_sight_range(agent_id) + elif range == "shoot_range": + unit_range = self.unit_shoot_range(agent_id) + else: + raise Exception("Range argument not recognised") + if distance > unit_range: + return False + x_diff = pos.x - ally_pos.x + x_diff = max(x_diff, EPS) if x_diff > 0 else min(x_diff, -EPS) + obj_angle = np.arctan((pos.y - ally_pos.y) / x_diff) + x = self.fov_directions[agent_id][0] + x = max(x, EPS) if x_diff > 0 else min(x, -EPS) + fov_angle = np.arctan(self.fov_directions[agent_id][1] / x) + return np.abs(obj_angle - fov_angle) < self.conic_fov_angle / 2 + + def get_obs_agent(self, agent_id, fully_observable=False): + """Returns observation for agent_id. The observation is composed of: + + - agent movement features (where it can move to, height information + and pathing grid) + - enemy features (available_to_attack, health, relative_x, relative_y, + shield, unit_type) + - ally features (visible, distance, relative_x, relative_y, shield, + unit_type) + - agent unit features (health, shield, unit_type) + + All of this information is flattened and concatenated into a list, + in the aforementioned order. To know the sizes of each of the + features inside the final list of features, take a look at the + functions ``get_obs_move_feats_size()``, + ``get_obs_enemy_feats_size()``, ``get_obs_ally_feats_size()`` and + ``get_obs_own_feats_size()``. + + The size of the observation vector may vary, depending on the + environment configuration and type of units present in the map. + For instance, non-Protoss units will not have shields, movement + features may or may not include terrain height and pathing grid, + unit_type is not included if there is only one type of unit in the + map etc.). + + NOTE: Agents should have access only to their local observations + during decentralised execution. + + fully_observable: -- ignores sight range for a particular unit. + For Debugging purposes ONLY -- not a fair observation. + """ + unit = self.get_unit_by_id(agent_id) + + move_feats_dim = self.get_obs_move_feats_size() + enemy_feats_dim = self.get_obs_enemy_feats_size() + ally_feats_dim = self.get_obs_ally_feats_size() + own_feats_dim = self.get_obs_own_feats_size() + + move_feats = np.zeros(move_feats_dim, dtype=np.float32) + enemy_feats = np.zeros(enemy_feats_dim, dtype=np.float32) + ally_feats = np.zeros(ally_feats_dim, dtype=np.float32) + own_feats = np.zeros(own_feats_dim, dtype=np.float32) + + if ( + unit.health > 0 and self.obs_starcraft + ): # otherwise dead, return all zeros + x = unit.pos.x + y = unit.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Movement features. Do not need similar for looking + # around because this is always possible + avail_actions = self.get_avail_agent_actions(agent_id) + for m in range(self.n_actions_move): + move_feats[m] = avail_actions[m + 2] + + ind = self.n_actions_move + + if self.obs_pathing_grid: + move_feats[ + ind: ind + self.n_obs_pathing # noqa + ] = self.get_surrounding_pathing(unit) + ind += self.n_obs_pathing + + if self.obs_terrain_height: + move_feats[ind:] = self.get_surrounding_height(unit) + + # Enemy features + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + enemy_visible = ( + self.is_position_in_cone(agent_id, e_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (enemy_visible and e_unit.health > 0) or ( + e_unit.health > 0 and fully_observable + ): # visible and alive + # Sight range > shoot range + enemy_feats[e_id, 0] = avail_actions[ + self.n_actions_no_attack + e_id + ] # available + enemy_feats[e_id, 1] = dist / sight_range # distance + enemy_feats[e_id, 2] = ( + e_x - x + ) / sight_range # relative X + enemy_feats[e_id, 3] = ( + e_y - y + ) / sight_range # relative Y + show_enemy = ( + self.mask_enemies + and not self.enemy_mask[agent_id][e_id] + ) or not self.mask_enemies + ind = 4 + if self.obs_all_health and show_enemy: + enemy_feats[e_id, ind] = ( + e_unit.health / e_unit.health_max + ) # health + ind += 1 + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_feats[e_id, ind] = ( + e_unit.shield / max_shield + ) # shield + ind += 1 + + if self.unit_type_bits > 0 and show_enemy: + type_id = self.get_unit_type_id(e_unit, False) + enemy_feats[e_id, ind + type_id] = 1 # unit type + + # Ally features + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id != agent_id + ] + for i, al_id in enumerate(al_ids): + + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + ally_visible = ( + self.is_position_in_cone(agent_id, al_unit.pos) + if self.conic_fov + else dist < sight_range + ) + if (ally_visible and al_unit.health > 0) or ( + al_unit.health > 0 and fully_observable + ): # visible and alive + ally_feats[i, 0] = 1 # visible + ally_feats[i, 1] = dist / sight_range # distance + ally_feats[i, 2] = (al_x - x) / sight_range # relative X + ally_feats[i, 3] = (al_y - y) / sight_range # relative Y + + ind = 4 + if self.obs_all_health: + if not self.stochastic_health: + ally_feats[i, ind] = ( + al_unit.health / al_unit.health_max + ) # health + ind += 1 + elif self.observe_teammate_health: + ally_feats[i, ind] = self._compute_health( + agent_id=al_id, unit=al_unit + ) + ind += 1 + elif self.zero_pad_health: + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_feats[i, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + if self.stochastic_attack and self.observe_attack_probs: + ally_feats[i, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + elif ( + self.stochastic_attack + and self.zero_pad_stochastic_attack + ): + ind += 1 + + if self.stochastic_health and self.observe_teammate_health: + ally_feats[i, ind] = self.agent_health_levels[al_id] + ind += 1 + elif self.stochastic_health and self.zero_pad_health: + ind += 1 + if self.unit_type_bits > 0 and ( + not self.replace_teammates + or self.observe_teammate_types + ): + type_id = self.get_unit_type_id(al_unit, True) + ally_feats[i, ind + type_id] = 1 + ind += self.unit_type_bits + elif self.unit_type_bits > 0 and self.zero_pad_unit_types: + ind += self.unit_type_bits + if self.obs_last_action: + ally_feats[i, ind:] = self.last_action[al_id] + + # Own features + ind = 0 + if self.obs_own_health: + if not self.stochastic_health: + own_feats[ind] = unit.health / unit.health_max + else: + own_feats[ind] = self._compute_health(agent_id, unit) + ind += 1 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(unit) + own_feats[ind] = unit.shield / max_shield + ind += 1 + + if self.stochastic_attack: + own_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + own_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.obs_own_pos: + own_feats[ind] = x / self.map_x + own_feats[ind + 1] = y / self.map_y + ind += 2 + if self.conic_fov: + own_feats[ind: ind + 2] = self.fov_directions[agent_id] + ind += 2 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + own_feats[ind + type_id] = 1 + if self.obs_starcraft: + agent_obs = np.concatenate( + ( + move_feats.flatten(), + enemy_feats.flatten(), + ally_feats.flatten(), + own_feats.flatten(), + ) + ) + + if self.obs_timestep_number: + if self.obs_starcraft: + agent_obs = np.append( + agent_obs, self._episode_steps / self.episode_limit + ) + else: + agent_obs = np.zeros(1, dtype=np.float32) + agent_obs[:] = self._episode_steps / self.episode_limit + + if self.debug: + logging.debug("Obs Agent: {}".format(agent_id).center(60, "-")) + logging.debug( + "Avail. actions {}".format( + self.get_avail_agent_actions(agent_id) + ) + ) + logging.debug("Move feats {}".format(move_feats)) + logging.debug("Enemy feats {}".format(enemy_feats)) + logging.debug("Ally feats {}".format(ally_feats)) + logging.debug("Own feats {}".format(own_feats)) + + return agent_obs + + def get_obs(self): + """Returns all agent observations in a list. + NOTE: Agents should have access only to their local observations + during decentralised execution. + """ + agents_obs = [ + self.get_obs_agent(i, fully_observable=self.fully_observable) + for i in range(self.n_agents) + ] + return agents_obs + + def get_capabilities_agent(self, agent_id): + unit = self.get_unit_by_id(agent_id) + cap_feats = np.zeros(self.get_cap_size(), dtype=np.float32) + + ind = 0 + if self.stochastic_attack: + cap_feats[ind] = self.agent_attack_probabilities[agent_id] + ind += 1 + if self.stochastic_health: + cap_feats[ind] = self.agent_health_levels[agent_id] + ind += 1 + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(unit, True) + cap_feats[ind + type_id] = 1 + + return cap_feats + + def get_capabilities(self): + """Returns all agent capabilities in a list.""" + agents_cap = [ + self.get_capabilities_agent(i) for i in range(self.n_agents) + ] + agents_cap = np.concatenate(agents_cap, axis=0).astype(np.float32) + return agents_cap + + def get_state(self): + """Returns the global state. + NOTE: This function should not be used during decentralised execution. + """ + if self.obs_instead_of_state: + obs_concat = np.concatenate(self.get_obs(), axis=0).astype( + np.float32 + ) + return obs_concat + + state_dict = self.get_state_dict() + + state = np.append( + state_dict["allies"].flatten(), state_dict["enemies"].flatten() + ) + if "last_action" in state_dict: + state = np.append(state, state_dict["last_action"].flatten()) + if "timestep" in state_dict: + state = np.append(state, state_dict["timestep"]) + + state = state.astype(dtype=np.float32) + + if self.debug: + logging.debug("STATE".center(60, "-")) + logging.debug("Ally state {}".format(state_dict["allies"])) + logging.debug("Enemy state {}".format(state_dict["enemies"])) + if self.state_last_action: + logging.debug("Last actions {}".format(self.last_action)) + + return state + + def get_ally_num_attributes(self): + return len(self.ally_state_attr_names) + len( + self.capability_attr_names + ) + + def get_enemy_num_attributes(self): + return len(self.enemy_state_attr_names) + + def get_state_dict(self): + """Returns the global state as a dictionary. + + - allies: numpy array containing agents and their attributes + - enemies: numpy array containing enemies and their attributes + - last_action: numpy array of previous actions for each agent + - timestep: current no. of steps divided by total no. of steps + + NOTE: This function should not be used during decentralised execution. + """ + + # number of features equals the number of attribute names + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + ally_state = np.zeros((self.n_agents, nf_al)) + enemy_state = np.zeros((self.n_enemies, nf_en)) + + center_x = self.map_x / 2 + center_y = self.map_y / 2 + + for al_id, al_unit in self.agents.items(): + if al_unit.health > 0: + x = al_unit.pos.x + y = al_unit.pos.y + max_cd = self.unit_max_cooldown(al_unit) + if not self.stochastic_health: + ally_state[al_id, 0] = ( + al_unit.health / al_unit.health_max + ) # health + else: + ally_state[al_id, 0] = self._compute_health(al_id, al_unit) + if ( + self.map_type in ["MMM", "terran_gen"] + and al_unit.unit_type == self.medivac_id + ): + ally_state[al_id, 1] = al_unit.energy / max_cd # energy + else: + ally_state[al_id, 1] = ( + al_unit.weapon_cooldown / max_cd + ) # cooldown + ally_state[al_id, 2] = ( + x - center_x + ) / self.max_distance_x # relative X + ally_state[al_id, 3] = ( + y - center_y + ) / self.max_distance_y # relative Y + + ind = 4 + if self.shield_bits_ally > 0: + max_shield = self.unit_max_shield(al_unit) + ally_state[al_id, ind] = ( + al_unit.shield / max_shield + ) # shield + ind += 1 + + if self.stochastic_attack: + ally_state[al_id, ind] = self.agent_attack_probabilities[ + al_id + ] + ind += 1 + if self.stochastic_health: + ally_state[al_id, ind] = self.agent_health_levels[al_id] + ind += 1 + if self.conic_fov: + ally_state[al_id, ind: ind + 2] = self.fov_directions[ + al_id + ] + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(al_unit, True) + ally_state[al_id, type_id - self.unit_type_bits] = 1 + + for e_id, e_unit in self.enemies.items(): + if e_unit.health > 0: + x = e_unit.pos.x + y = e_unit.pos.y + + enemy_state[e_id, 0] = ( + e_unit.health / e_unit.health_max + ) # health + enemy_state[e_id, 1] = ( + x - center_x + ) / self.max_distance_x # relative X + enemy_state[e_id, 2] = ( + y - center_y + ) / self.max_distance_y # relative Y + + if self.shield_bits_enemy > 0: + max_shield = self.unit_max_shield(e_unit) + enemy_state[e_id, 3] = e_unit.shield / max_shield # shield + + if self.unit_type_bits > 0: + type_id = self.get_unit_type_id(e_unit, False) + enemy_state[e_id, type_id - self.unit_type_bits] = 1 + + state = {"allies": ally_state, "enemies": enemy_state} + + if self.state_last_action: + state["last_action"] = self.last_action + if self.state_timestep_number: + state["timestep"] = self._episode_steps / self.episode_limit + + return state + + def get_obs_enemy_feats_size(self): + """Returns the dimensions of the matrix containing enemy features. + Size is n_enemies x n_features. + """ + nf_en = 4 + self.unit_type_bits + + if self.obs_all_health: + nf_en += 1 + self.shield_bits_enemy + + return self.n_enemies, nf_en + + def get_obs_ally_feats_size(self): + """Returns the dimensions of the matrix containing ally features. + Size is n_allies x n_features. + """ + nf_al = 4 + nf_cap = self.get_obs_ally_capability_size() + + if self.obs_all_health: + nf_al += 1 + self.shield_bits_ally + + if self.obs_last_action: + nf_al += self.n_actions + + return self.n_agents - 1, nf_al + nf_cap + + def get_obs_own_feats_size(self): + """ + Returns the size of the vector containing the agents' own features. + """ + own_feats = self.get_cap_size() + if self.obs_own_health and self.obs_starcraft: + own_feats += 1 + self.shield_bits_ally + if self.conic_fov and self.obs_starcraft: + own_feats += 2 + if self.obs_own_pos and self.obs_starcraft: + own_feats += 2 + return own_feats + + def get_obs_move_feats_size(self): + """Returns the size of the vector containing the agents's movement- + related features. + """ + move_feats = self.n_actions_move + if self.obs_pathing_grid: + move_feats += self.n_obs_pathing + if self.obs_terrain_height: + move_feats += self.n_obs_height + + return move_feats + + def get_obs_ally_capability_size(self): + """Returns the size of capabilities observed by teammates.""" + cap_feats = self.unit_type_bits + if self.stochastic_attack and ( + self.zero_pad_stochastic_attack or self.observe_attack_probs + ): + cap_feats += 1 + if self.stochastic_health and ( + self.observe_teammate_health or self.zero_pad_health + ): + cap_feats += 1 + + return cap_feats + + def get_cap_size(self): + """Returns the size of the own capabilities of the agent.""" + cap_feats = 0 + if self.stochastic_attack: + cap_feats += 1 + if self.stochastic_health: + cap_feats += 1 + if self.unit_type_bits > 0: + cap_feats += self.unit_type_bits + + return cap_feats + + def get_obs_size(self): + """Returns the size of the observation.""" + own_feats = self.get_obs_own_feats_size() + move_feats = self.get_obs_move_feats_size() + + n_enemies, n_enemy_feats = self.get_obs_enemy_feats_size() + n_allies, n_ally_feats = self.get_obs_ally_feats_size() + + enemy_feats = n_enemies * n_enemy_feats + ally_feats = n_allies * n_ally_feats + if self.obs_starcraft: + return ( + self.obs_timestep_number + + move_feats + + enemy_feats + + ally_feats + + own_feats + ) + else: + return 1 if self.obs_timestep_number else 0 + + def get_state_size(self): + """Returns the size of the global state.""" + if self.obs_instead_of_state: + return self.get_obs_size() * self.n_agents + + nf_al = self.get_ally_num_attributes() + nf_en = self.get_enemy_num_attributes() + + enemy_state = self.n_enemies * nf_en + ally_state = self.n_agents * nf_al + + size = enemy_state + ally_state + + if self.state_last_action: + size += self.n_agents * self.n_actions + if self.state_timestep_number: + size += 1 + + return size + + def get_visibility_matrix(self): + """Returns a boolean numpy array of dimensions + (n_agents, n_agents + n_enemies) indicating which units + are visible to each agent. + """ + arr = np.zeros( + (self.n_agents, self.n_agents + self.n_enemies), + dtype=np.bool, + ) + + for agent_id in range(self.n_agents): + current_agent = self.get_unit_by_id(agent_id) + if current_agent.health > 0: # it agent not dead + x = current_agent.pos.x + y = current_agent.pos.y + sight_range = self.unit_sight_range(agent_id) + + # Enemies + for e_id, e_unit in self.enemies.items(): + e_x = e_unit.pos.x + e_y = e_unit.pos.y + dist = self.distance(x, y, e_x, e_y) + + if dist < sight_range and e_unit.health > 0: + # visible and alive + arr[agent_id, self.n_agents + e_id] = 1 + + # The matrix for allies is filled symmetrically + al_ids = [ + al_id for al_id in range(self.n_agents) if al_id > agent_id + ] + for _, al_id in enumerate(al_ids): + al_unit = self.get_unit_by_id(al_id) + al_x = al_unit.pos.x + al_y = al_unit.pos.y + dist = self.distance(x, y, al_x, al_y) + + if dist < sight_range and al_unit.health > 0: + # visible and alive + arr[agent_id, al_id] = arr[al_id, agent_id] = 1 + + return arr + + def get_unit_type_id(self, unit, ally): + """Returns the ID of unit type in the given scenario.""" + + if self.map_type == "protoss_gen": + if unit.unit_type in (self.stalker_id, Protoss.Stalker): + return 0 + if unit.unit_type in (self.zealot_id, Protoss.Zealot): + return 1 + if unit.unit_type in (self.colossus_id, Protoss.Colossus): + return 2 + raise AttributeError() + if self.map_type == "terran_gen": + if unit.unit_type in (self.marine_id, Terran.Marine): + return 0 + if unit.unit_type in (self.marauder_id, Terran.Marauder): + return 1 + if unit.unit_type in (self.medivac_id, Terran.Medivac): + return 2 + raise AttributeError() + + if self.map_type == "zerg_gen": + if unit.unit_type in (self.zergling_id, Zerg.Zergling): + return 0 + if unit.unit_type in (self.hydralisk_id, Zerg.Hydralisk): + return 1 + if unit.unit_type in (self.baneling_id, Zerg.Baneling): + return 2 + raise AttributeError() + + # Old stuff + if ally: # use new SC2 unit types + type_id = unit.unit_type - self._min_unit_type + + if self.map_type == "stalkers_and_zealots": + # id(Stalker) = 74, id(Zealot) = 73 + type_id = unit.unit_type - 73 + elif self.map_type == "colossi_stalkers_zealots": + # id(Stalker) = 74, id(Zealot) = 73, id(Colossus) = 4 + if unit.unit_type == 4: + type_id = 0 + elif unit.unit_type == 74: + type_id = 1 + else: + type_id = 2 + elif self.map_type == "bane": + if unit.unit_type == 9: + type_id = 0 + else: + type_id = 1 + elif self.map_type == "MMM": + if unit.unit_type == 51: + type_id = 0 + elif unit.unit_type == 48: + type_id = 1 + else: + type_id = 2 + + return type_id + + def get_avail_agent_actions(self, agent_id): + """Returns the available actions for agent_id.""" + unit = self.get_unit_by_id(agent_id) + if unit.health > 0: + # cannot choose no-op when alive + avail_actions = [0] * self.n_actions + + # stop should be allowed + avail_actions[1] = 1 + + # see if we can move + if self.can_move(unit, Direction.NORTH): + avail_actions[2] = 1 + if self.can_move(unit, Direction.SOUTH): + avail_actions[3] = 1 + if self.can_move(unit, Direction.EAST): + avail_actions[4] = 1 + if self.can_move(unit, Direction.WEST): + avail_actions[5] = 1 + + # Can attack only alive units that are alive in the shooting range + shoot_range = self.unit_shoot_range(agent_id) + + target_items = self.enemies.items() + if self.map_type in ["MMM", "terran_gen"] and unit.unit_type == self.medivac_id: + # Medivacs cannot heal themselves or other flying units + target_items = [ + (t_id, t_unit) + for (t_id, t_unit) in self.agents.items() + if t_unit.unit_type != self.medivac_id + ] + # should we only be able to target people in the cone? + for t_id, t_unit in target_items: + if t_unit.health > 0: + dist = self.distance( + unit.pos.x, unit.pos.y, t_unit.pos.x, t_unit.pos.y + ) + can_shoot = ( + dist <= shoot_range + if not self.conic_fov + else self.is_position_in_cone( + agent_id, t_unit.pos, range="shoot_range" + ) + ) + if can_shoot: + avail_actions[t_id + self.n_actions_no_attack] = 1 + + return avail_actions + + else: + # only no-op allowed + return [1] + [0] * (self.n_actions - 1) + + def get_avail_actions(self): + """Returns the available actions of all agents in a list.""" + avail_actions = [] + for agent_id in range(self.n_agents): + avail_agent = self.get_avail_agent_actions(agent_id) + avail_actions.append(avail_agent) + return avail_actions + + def close(self): + """Close StarCraft II.""" + if self.renderer is not None: + self.renderer.close() + self.renderer = None + if self._sc2_proc: + self._sc2_proc.close() + + def seed(self): + """Returns the random seed used by the environment.""" + return self._seed + + def render(self, mode="human"): + if self.renderer is None: + from smac.env.starcraft2.render import StarCraft2Renderer + + self.renderer = StarCraft2Renderer(self, mode) + assert ( + mode == self.renderer.mode + ), "mode must be consistent across render calls" + return self.renderer.render(mode) + + def _kill_units(self, unit_tags): + debug_command = [ + d_pb.DebugCommand(kill_unit=d_pb.DebugKillUnit(tag=unit_tags)) + ] + self._controller.debug(debug_command) + + def _kill_all_units(self): + """Kill all units on the map. Steps controller and so can throw + exceptions""" + units = [unit.tag for unit in self._obs.observation.raw_data.units] + self._kill_units(units) + # check the units are dead + units = len(self._obs.observation.raw_data.units) + while len(self._obs.observation.raw_data.units) > 0: + self._controller.step(2) + self._obs = self._controller.observe() + + def _create_new_team(self, team, episode_config): + # unit_names = { + # self.id_to_unit_name_map[unit.unit_type] + # for unit in self.agents.values() + # } + # It's important to set the number of agents and enemies + # because we use that to identify whether all the units have + # been created successfully + + # TODO hardcoding init location. change this later for new maps + if not self.random_start: + ally_init_pos = [sc_common.Point2D(x=8, y=16)] * self.n_agents + # Spawning location of enemy units + enemy_init_pos = [sc_common.Point2D(x=24, y=16)] * self.n_enemies + else: + ally_init_pos = [ + sc_common.Point2D( + x=self.ally_start_positions[i][0], + y=self.ally_start_positions[i][1], + ) + for i in range(self.ally_start_positions.shape[0]) + ] + enemy_init_pos = [ + sc_common.Point2D( + x=self.enemy_start_positions[i][0], + y=self.enemy_start_positions[i][1], + ) + for i in range(self.enemy_start_positions.shape[0]) + ] + for unit_id, unit in enumerate(team): + unit_type_ally = self._convert_unit_name_to_unit_type( + unit, ally=True + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_ally, + owner=1, + pos=ally_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + unit_type_enemy = self._convert_unit_name_to_unit_type( + unit, ally=False + ) + debug_command = [ + d_pb.DebugCommand( + create_unit=d_pb.DebugCreateUnit( + unit_type=unit_type_enemy, + owner=2, + pos=enemy_init_pos[unit_id], + quantity=1, + ) + ) + ] + self._controller.debug(debug_command) + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def _convert_unit_name_to_unit_type(self, unit_name, ally=True): + if ally: + return self.ally_unit_map[unit_name] + else: + return self.enemy_unit_map[unit_name] + + def init_units(self, team, episode_config={}): + """Initialise the units.""" + if team: + # can use any value for min unit type because + # it is hardcoded based on the version + self._init_ally_unit_types(0) + self._create_new_team(team, episode_config) + while True: + # Sometimes not all units have yet been created by SC2 + self.agents = {} + self.enemies = {} + + ally_units = [ + unit + for unit in self._obs.observation.raw_data.units + if unit.owner == 1 + ] + ally_units_sorted = sorted( + ally_units, + key=attrgetter("unit_type", "pos.x", "pos.y"), + reverse=False, + ) + + for i in range(len(ally_units_sorted)): + self.agents[i] = ally_units_sorted[i] + if self.debug: + logging.debug( + "Unit {} is {}, x = {}, y = {}".format( + len(self.agents), + self.agents[i].unit_type, + self.agents[i].pos.x, + self.agents[i].pos.y, + ) + ) + + for unit in self._obs.observation.raw_data.units: + if unit.owner == 2: + self.enemies[len(self.enemies)] = unit + if self._episode_count == 0: + self.max_reward += unit.health_max + unit.shield_max + + if self._episode_count == 0 and not team: + min_unit_type = min( + unit.unit_type for unit in self.agents.values() + ) + self._init_ally_unit_types(min_unit_type) + + all_agents_created = len(self.agents) == self.n_agents + all_enemies_created = len(self.enemies) == self.n_enemies + + self._unit_types = [ + unit.unit_type for unit in ally_units_sorted + ] + [ + unit.unit_type + for unit in self._obs.observation.raw_data.units + if unit.owner == 2 + ] + + # TODO move this to the start + if all_agents_created and all_enemies_created: # all good + return + + try: + self._controller.step(1) + self._obs = self._controller.observe() + except (protocol.ProtocolError, protocol.ConnectionError): + self.full_restart() + self.reset(episode_config=episode_config) + + def get_unit_types(self): + if self._unit_types is None: + warn( + "unit types have not been initialized yet, please call" + "env.reset() to populate this and call t1286he method again." + ) + + return self._unit_types + + def update_units(self): + """Update units after an environment step. + This function assumes that self._obs is up-to-date. + """ + n_ally_alive = 0 + n_enemy_alive = 0 + + # Store previous state + self.previous_ally_units = deepcopy(self.agents) + self.previous_enemy_units = deepcopy(self.enemies) + + for al_id, al_unit in self.agents.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if al_unit.tag == unit.tag: + self.agents[al_id] = unit + updated = True + n_ally_alive += 1 + break + + if not updated: # dead + al_unit.health = 0 + + for e_id, e_unit in self.enemies.items(): + updated = False + for unit in self._obs.observation.raw_data.units: + if e_unit.tag == unit.tag: + self.enemies[e_id] = unit + updated = True + n_enemy_alive += 1 + break + + if not updated: # dead + e_unit.health = 0 + + if ( + n_ally_alive == 0 + and n_enemy_alive > 0 + or self.only_medivac_left(ally=True) + ): + return -1 # lost + if ( + n_ally_alive > 0 + and n_enemy_alive == 0 + or self.only_medivac_left(ally=False) + ): + return 1 # won + if n_ally_alive == 0 and n_enemy_alive == 0: + return 0 + + return None + + def _register_unit_mapping(self, unit_name, unit_type_id): + self.id_to_unit_name_map[unit_type_id] = unit_name + self.unit_name_to_id_map[unit_name] = unit_type_id + + def _init_ally_unit_types(self, min_unit_type): + """Initialise ally unit types. Should be called once from the + init_units function. + """ + + self._min_unit_type = min_unit_type + + if "10gen_" in self.map_name: + num_rl_units = 9 + self._min_unit_type = ( + len(self._controller.data().units) - num_rl_units + ) + + self.baneling_id = self._min_unit_type + self.colossus_id = self._min_unit_type + 1 + self.hydralisk_id = self._min_unit_type + 2 + self.marauder_id = self._min_unit_type + 3 + self.marine_id = self._min_unit_type + 4 + self.medivac_id = self._min_unit_type + 5 + self.stalker_id = self._min_unit_type + 6 + self.zealot_id = self._min_unit_type + 7 + self.zergling_id = self._min_unit_type + 8 + + self.ally_unit_map = { + "baneling": self.baneling_id, + "colossus": self.colossus_id, + "hydralisk": self.hydralisk_id, + "marauder": self.marauder_id, + "marine": self.marine_id, + "medivac": self.medivac_id, + "stalker": self.stalker_id, + "zealot": self.zealot_id, + "zergling": self.zergling_id, + } + self.enemy_unit_map = { + "baneling": Zerg.Baneling, + "colossus": Protoss.Colossus, + "hydralisk": Zerg.Hydralisk, + "marauder": Terran.Marauder, + "marine": Terran.Marine, + "medivac": Terran.Medivac, + "stalker": Protoss.Stalker, + "zealot": Protoss.Zealot, + "zergling": Zerg.Zergling, + } + + else: + if self.map_type == "marines": + self.marine_id = min_unit_type + self._register_unit_mapping("marine", min_unit_type) + elif self.map_type == "stalkers_and_zealots": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + self.zealot_id = min_unit_type + 1 + self._register_unit_mapping("zealot", min_unit_type + 1) + elif self.map_type == "colossi_stalkers_zealots": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + self.stalker_id = min_unit_type + 1 + self._register_unit_mapping("stalker", min_unit_type + 1) + self.zealot_id = min_unit_type + 2 + self._register_unit_mapping("zealot", min_unit_type + 2) + elif self.map_type == "MMM": + self.marauder_id = min_unit_type + self._register_unit_mapping("marauder", min_unit_type) + self.marine_id = min_unit_type + 1 + self._register_unit_mapping("marine", min_unit_type + 1) + self.medivac_id = min_unit_type + 2 + self._register_unit_mapping("medivac", min_unit_type + 2) + elif self.map_type == "zealots": + self.zealot_id = min_unit_type + self._register_unit_mapping("zealot", min_unit_type) + elif self.map_type == "hydralisks": + self.hydralisk_id = min_unit_type + self._register_unit_mapping("hydralisk", min_unit_type) + elif self.map_type == "stalkers": + self.stalker_id = min_unit_type + self._register_unit_mapping("stalker", min_unit_type) + elif self.map_type == "colossus": + self.colossus_id = min_unit_type + self._register_unit_mapping("colossus", min_unit_type) + elif self.map_type == "bane": + self.baneling_id = min_unit_type + self._register_unit_mapping("baneling", min_unit_type) + self.zergling_id = min_unit_type + 1 + self._register_unit_mapping("zergling", min_unit_type + 1) + + def only_medivac_left(self, ally): + """Check if only Medivac units are left.""" + if self.map_type != "MMM" and self.map_type != "terran_gen": + return False + + if ally: + units_alive = [ + a + for a in self.agents.values() + if (a.health > 0 and a.unit_type != self.medivac_id) + ] + if len(units_alive) == 0: + return True + return False + else: + units_alive = [ + a + for a in self.enemies.values() + if (a.health > 0 and a.unit_type != Terran.Medivac) + ] + if len(units_alive) == 0: + return True + return False + + def get_unit_by_id(self, a_id): + """Get unit by ID.""" + return self.agents[a_id] + + def get_stats(self): + stats = { + "battles_won": self.battles_won, + "battles_game": self.battles_game, + "battles_draw": self.timeouts, + "win_rate": self.battles_won / self.battles_game, + "timeouts": self.timeouts, + "restarts": self.force_restarts, + } + return stats + + def get_env_info(self): + env_info = super().get_env_info() + env_info["agent_features"] = ( + self.ally_state_attr_names + self.capability_attr_names + ) + env_info["enemy_features"] = self.enemy_state_attr_names + return env_info diff --git a/src/envs/smac_v2/official/wrapper.py b/src/envs/smac_v2/official/wrapper.py new file mode 100644 index 0000000..cf8d72a --- /dev/null +++ b/src/envs/smac_v2/official/wrapper.py @@ -0,0 +1,88 @@ +from .distributions import get_distribution +from .starcraft2 import StarCraft2Env +from .starcraft2_hxt import StarCraft2Env as StarCraft2EnvMoveWithFov +from envs.multiagentenv import MultiAgentEnv + + +class StarCraftCapabilityEnvWrapper(MultiAgentEnv): + def __init__(self, **kwargs): + self.distribution_config = kwargs["capability_config"] + self.env_key_to_distribution_map = {} + self._parse_distribution_config() + change_fov_with_move = kwargs.pop("change_fov_with_move") + self.env = StarCraft2EnvMoveWithFov(**kwargs) if change_fov_with_move else StarCraft2Env(**kwargs) + assert ( + self.distribution_config.keys() + == kwargs["capability_config"].keys() + ), "Must give distribution config and capability config the same keys" + + def _parse_distribution_config(self): + for env_key, config in self.distribution_config.items(): + if env_key == "n_units": + continue + config["env_key"] = env_key + # add n_units key + config["n_units"] = self.distribution_config["n_units"] + distribution = get_distribution(config["dist_type"])(config) + self.env_key_to_distribution_map[env_key] = distribution + + def reset(self): + reset_config = {} + for distribution in self.env_key_to_distribution_map.values(): + reset_config = {**reset_config, **distribution.generate()} + + return self.env.reset(reset_config) + + def __getattr__(self, name): + if hasattr(self.env, name): + return getattr(self.env, name) + else: + raise AttributeError + + def get_obs(self): + return self.env.get_obs() + + def get_state(self): + return self.env.get_state() + + def get_avail_actions(self): + return self.env.get_avail_actions() + + def get_env_info(self): + return self.env.get_env_info() + + def get_obs_size(self): + return self.env.get_obs_size() + + def get_state_size(self): + return self.env.get_state_size() + + def get_total_actions(self): + return self.env.get_total_actions() + + def get_capabilities(self): + return self.env.get_capabilities() + + def get_obs_agent(self, agent_id): + return self.env.get_obs_agent(agent_id) + + def get_avail_agent_actions(self, agent_id): + return self.env.get_avail_agent_actions(agent_id) + + def render(self): + return self.env.render() + + def step(self, actions): + return self.env.step(actions) + + def get_stats(self): + return self.env.get_stats() + + def full_restart(self): + return self.env.full_restart() + + def save_replay(self): + self.env.save_replay() + + def close(self): + return self.env.close() diff --git a/src/learners/FeUdal_learner.py b/src/learners/FeUdal_learner.py new file mode 100644 index 0000000..4db9028 --- /dev/null +++ b/src/learners/FeUdal_learner.py @@ -0,0 +1,257 @@ +import copy +from components.episode_buffer import EpisodeBatch +from modules.mixers.vdn import VDNMixer +from modules.mixers.qmix import QMixer +import torch +from torch.optim import RMSprop +import torch.nn.functional as F +#from modules.critics.feudal_critic import FeudalCritic + +import numpy as np + +class FeudalLearner: + def __init__(self, mac, scheme, logger, args): + self.args = args + self.mac = mac + self.logger = logger + + self.manager_params = list(mac.manager_parameters()) + self.worker_params = list(mac.worker_parameters()) + + + self.last_target_update_episode = 0 + + self.mixer = None + if args.mixer is not None: + if args.mixer == "vdn": + self.mixer = VDNMixer() + elif args.mixer == "qmix": + self.mixer = QMixer(args) + else: + raise ValueError("Mixer {} not recognised.".format(args.mixer)) + self.worker_params += list(self.mixer.parameters()) + self.target_mixer = copy.deepcopy(self.mixer) + + # Optimise manager's parameters + self.manager_optimiser = RMSprop(params=self.manager_params, lr=self.args.lr, alpha=self.args.optim_alpha, eps=self.args.optim_eps) + self.worker_optimiser = RMSprop(params=self.worker_params, lr=self.args.lr, alpha=self.args.optim_alpha, eps=self.args.optim_eps) + + # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC + self.target_mac = copy.deepcopy(mac) + + self.enable_parallel_computing = (not self.args.use_cuda) and getattr(self.args, 'enable_parallel_computing', + False) + # self.enable_parallel_computing = False + if self.enable_parallel_computing: + from multiprocessing import Pool + # Multiprocessing pool for parallel computing. + self.pool = Pool(1) + + self.log_stats_t = -self.args.learner_log_interval - 1 + + def train(self, batch: EpisodeBatch, t_env: int, episode_num: int, show_demo=False, save_data=None): + if self.args.use_cuda and str(self.mac.get_device()) == "cpu": + self.mac.cuda() + + # Get the relevant quantities + rewards = batch["reward"][:, :-1] + actions = batch["actions"][:, :-1] + terminated = batch["terminated"][:, :-1].float() + mask = batch["filled"][:, :-1].float() + mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1]) + avail_actions = batch["avail_actions"] + + states = batch["obs"][:, :-1] # 倒數第二個時間步 + next_states = batch["obs"][:, 1:] # 最後一個時間步 + + # Calculate estimated Q-Values + mac_out, goals_out, values_out = [], [], [] + self.mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + agent_outs, goals, values = self.mac.forward(batch, t=t) + mac_out.append(agent_outs) + goals_out.append(goals) + values_out.append(values) + mac_out = torch.stack(mac_out, dim=1) # Concat over time + goals_out = torch.stack(goals_out, dim=1) # Concat over time + values_out = torch.stack(values_out, dim=1) # Concat over time + + + # Use goal_out to influence action selection or target calculation + # Example: Modify chosen_action_qvals calculation using goal_out + chosen_action_qvals = torch.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3) # Remove the last dim + # Modify chosen_action_qvals with goal_out if necessary + # chosen_action_qvals = some_function(chosen_action_qvals, goal_out) + + x_mac_out = mac_out.clone().detach() + x_mac_out[avail_actions == 0] = -9999999 + max_action_qvals, max_action_index = x_mac_out[:, :-1].max(dim=3) + + max_action_index = max_action_index.detach().unsqueeze(3) + is_max_action = (max_action_index == actions).int().float() + + if show_demo: + q_i_data = chosen_action_qvals.detach().cpu().numpy() + q_data = (max_action_qvals - chosen_action_qvals).detach().cpu().numpy() + + # Calculate the Q-Values necessary for the target + target_mac_out = [] + self.target_mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + target_agent_outs, _, _ = self.target_mac.forward(batch, t=t) + target_mac_out.append(target_agent_outs) + + # We don't need the first timesteps Q-Value estimate for calculating targets + target_mac_out = torch.stack(target_mac_out[1:], dim=1) # Concat across time + + # Max over target Q-Values + if self.args.double_q: + # Get actions that maximise live Q (for double q-learning) + mac_out_detach = mac_out.clone().detach() + mac_out_detach[avail_actions == 0] = -9999999 + cur_max_actions = mac_out_detach[:, 1:].max(dim=3, keepdim=True)[1] + target_max_qvals = torch.gather(target_mac_out, 3, cur_max_actions).squeeze(3) + else: + target_max_qvals = target_mac_out.max(dim=3)[0] + + # Mix + if self.mixer is not None: + chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1]) + target_max_qvals = self.target_mixer(target_max_qvals, batch["state"][:, 1:]) + + if show_demo: + tot_q_data = chosen_action_qvals.detach().cpu().numpy() + tot_target = targets.detach().cpu().numpy() + if self.mixer == None: + tot_q_data = np.mean(tot_q_data, axis=2) + tot_target = np.mean(tot_target, axis=2) + + print('action_pair_%d_%d' % (save_data[0], save_data[1]), np.squeeze(q_data[:, 0]), + np.squeeze(q_i_data[:, 0]), np.squeeze(tot_q_data[:, 0]), np.squeeze(tot_target[:, 0])) + self.logger.log_stat('action_pair_%d_%d' % (save_data[0], save_data[1]), + np.squeeze(tot_q_data[:, 0]), t_env) + return + + #----------------------------- Calculate worker loss ----------------------------------- + # 1. 計算內部獎勵 + c = self.args.c # c 步數 + states = batch["obs"][:, :-c] # s_t + future_states = batch["obs"][:, c:] # s_{t+c} + state_diff = future_states - states # 計算狀態差異 + goals = goals_out[:, :-c] # 對應時間步的目標 + # print("state_diff.shape: ", state_diff.shape) + # print("goals.shape: ", goals.shape) + + # 計算內部獎勵 (使用餘弦相似度) + temp_state_diff = state_diff.reshape(-1, self.args.state_dim) + temp_goals = goals.reshape(-1, self.args.state_dim) + cos_sim = F.cosine_similarity(temp_state_diff, temp_goals, dim=-1) + cos_sim = cos_sim.view(state_diff.shape[0], state_diff.shape[1], -1) + intrinsic_rewards = (cos_sim / c).mean(dim=2, keepdim=True) + + # 2. 組合內部和外部獎勵 + combined_rewards = (1 - self.args.intrinsic_rewards_alpha) * rewards + \ + self.args.intrinsic_rewards_alpha * intrinsic_rewards + + # 3. 使用組合獎勵計算 TD 誤差 + targets = combined_rewards + self.args.gamma * (1 - terminated) * target_max_qvals + td_error = (chosen_action_qvals - targets.detach()) + mask = mask.expand_as(td_error) + # 0-out the targets that came from padded data + masked_td_error = td_error * mask + worker_loss = (masked_td_error ** 2).sum() + + # 4. 優化 Worker + self.worker_optimiser.zero_grad() + worker_loss.backward(retain_graph=True) + grad_norm_worker = torch.nn.utils.clip_grad_norm_(self.worker_params, self.args.grad_norm_clip) + self.worker_optimiser.step() + + + masked_hit_prob = torch.mean(is_max_action, dim=2) * mask + hit_prob = masked_hit_prob.sum() / mask.sum() + + #----------------------------- Calculate manager loss ----------------------------------- + # 1. 計算狀態差異和目標 + states = batch["obs"][:, :-c] # s_t + future_states = batch["obs"][:, c:] # s_{t+c} + state_diff = future_states - states + goals = goals_out[:, :-c] + values = values_out[:, :-c] # 使用 manager 輸出的 values + + # 2. 計算優勢函數 - 為每個agent分別計算 + rewards_expanded = rewards.unsqueeze(2).expand(-1, -1, self.args.n_agents, -1).squeeze(-1) # [batch_size, seq_len-1, n_agents] + + # values_out: [batch_size, seq_len-1, n_agents] + next_values = values_out[:, c:].squeeze(-1) # [batch_size, seq_len-c, n_agents] + current_values = values.squeeze(-1) # [batch_size, seq_len-c, n_agents] + + # 3. 為每個agent計算目標值和優勢 + target_values = rewards_expanded + (self.args.gamma ** c) * next_values # [batch_size, seq_len-c, n_agents] + advantages = rewards_expanded - current_values # [batch_size, seq_len-c, n_agents] + + # 4. 計算策略梯度 + temp_state_diff = state_diff.reshape(-1, self.args.state_dim) + temp_goals = goals.reshape(-1, self.args.state_dim) + cos_sim = F.cosine_similarity(temp_state_diff, temp_goals, dim=-1) + cos_sim = cos_sim.view(state_diff.shape[0], state_diff.shape[1], -1) # [batch_size, seq_len-c, n_agents] + + # 5. Manager 的損失 + value_loss = F.mse_loss(current_values, target_values.detach()) + manager_loss = -(advantages.detach() * cos_sim).sum() + total_manager_loss = manager_loss + self.args.vf_coef * value_loss + + # 6. 優化 Manager + self.manager_optimiser.zero_grad() + total_manager_loss.backward() + grad_norm_manager = torch.nn.utils.clip_grad_norm_(self.manager_params, self.args.grad_norm_clip) + self.manager_optimiser.step() + + + if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0: + self._update_targets() + self.last_target_update_episode = episode_num + + if t_env - self.log_stats_t >= self.args.learner_log_interval: + self.logger.log_stat("worker_loss", worker_loss.item(), t_env) + self.logger.log_stat("manager_loss", total_manager_loss.item(), t_env) + self.logger.log_stat("manager_cos_sim", cos_sim.mean().item(), t_env) + self.logger.log_stat("manager_advantage", advantages.mean().item(), t_env) + self.logger.log_stat("hit_prob", hit_prob.item(), t_env) + self.logger.log_stat("grad_norm_manager", grad_norm_manager, t_env) + self.logger.log_stat("grad_norm_worker", grad_norm_worker, t_env) + mask_elems = mask.sum().item() + self.logger.log_stat("td_error_abs", (masked_td_error.abs().sum().item()/mask_elems), t_env) + self.logger.log_stat("q_taken_mean", (chosen_action_qvals * mask).sum().item()/(mask_elems * self.args.n_agents), t_env) + self.logger.log_stat("target_mean", (targets * mask).sum().item()/(mask_elems * self.args.n_agents), t_env) + self.log_stats_t = t_env + + + def _update_targets(self): + self.target_mac.load_state(self.mac) + if self.mixer is not None: + self.target_mixer.load_state_dict(self.mixer.state_dict()) + self.logger.console_logger.info("Updated target network") + + def cuda(self): + self.mac.cuda() + self.target_mac.cuda() + if self.mixer is not None: + self.mixer.cuda() + self.target_mixer.cuda() + + def save_models(self, path): + self.mac.save_models(path) + if self.mixer is not None: + torch.save(self.mixer.state_dict(), "{}/mixer.torch".format(path)) + torch.save(self.worker_optimiser.state_dict(), "{}/worker_opt.torch".format(path)) + torch.save(self.manager_optimiser.state_dict(), "{}/manager_opt.torch".format(path)) + + def load_models(self, path): + self.mac.load_models(path) + # Not quite right but I don't want to save target networks + self.target_mac.load_models(path) + if self.mixer is not None: + self.mixer.load_state_dict(torch.load("{}/mixer.torch".format(path), map_location=lambda storage, loc: storage)) + self.worker_optimiser.load_state_dict(torch.load("{}/worker_opt.torch".format(path), map_location=lambda storage, loc: storage)) + self.manager_optimiser.load_state_dict(torch.load("{}/manager_opt.torch".format(path), map_location=lambda storage, loc: storage)) diff --git a/src/learners/__init__.py b/src/learners/__init__.py new file mode 100644 index 0000000..c9d885a --- /dev/null +++ b/src/learners/__init__.py @@ -0,0 +1,10 @@ +from .dmaq_qatten_learner import DMAQ_qattenLearner +from .nq_learner import NQLearner +from .nq_learner_data_augmentation import NQLearnerDataAugmentation +from .FeUdal_learner import FeudalLearner +REGISTRY = {} + +REGISTRY["nq_learner"] = NQLearner +REGISTRY["dmaq_qatten_learner"] = DMAQ_qattenLearner +REGISTRY["q_learner_data_augmentation"] = NQLearnerDataAugmentation +REGISTRY["feudal_learner"] = FeudalLearner diff --git a/src/learners/dmaq_qatten_learner.py b/src/learners/dmaq_qatten_learner.py new file mode 100644 index 0000000..701da37 --- /dev/null +++ b/src/learners/dmaq_qatten_learner.py @@ -0,0 +1,233 @@ +# From https://github.com/wjh720/QPLEX/, added here for convenience. +import copy +import time + +import torch as th +from torch.optim import Adam + +from components.episode_buffer import EpisodeBatch +from modules.mixers.dmaq_general import DMAQer +from utils.rl_utils import build_td_lambda_targets +from utils.th_utils import get_parameters_num + + +class DMAQ_qattenLearner: + def __init__(self, mac, scheme, logger, args): + self.args = args + self.mac = mac + self.logger = logger + + self.params = list(mac.parameters()) + + self.last_target_update_episode = 0 + + self.mixer = None + if args.mixer is not None: + if args.mixer == "dmaq": + self.mixer = DMAQer(args) + else: + raise ValueError("Mixer {} not recognised.".format(args.mixer)) + self.params += list(self.mixer.parameters()) + self.target_mixer = copy.deepcopy(self.mixer) + + self.optimiser = Adam(params=self.params, lr=args.lr) + + print('Mixer Size: ') + print(get_parameters_num(self.mixer.parameters())) + + # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC + self.target_mac = copy.deepcopy(mac) + self.log_stats_t = -self.args.learner_log_interval - 1 + self.n_actions = self.args.n_actions + self.train_t = 0 + self.avg_time = 0 + + def sub_train(self, batch: EpisodeBatch, t_env: int, episode_num: int, mac, mixer, optimiser, params, + save_data=None, split_data=False): + start_time = time.time() + + # Get the relevant quantities + rewards = batch["reward"][:, :-1] + actions = batch["actions"][:, :-1] + terminated = batch["terminated"][:, :-1].float() + mask = batch["filled"][:, :-1].float() + mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1]) + avail_actions = batch["avail_actions"] + actions_onehot = batch["actions_onehot"][:, :-1] + + # Calculate estimated Q-Values + self.mac.set_train_mode() + mac_out = [] + mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + agent_outs = mac.forward(batch, t=t) + mac_out.append(agent_outs) + mac_out = th.stack(mac_out, dim=1) # Concat over time + + # Pick the Q-Values for the actions taken by each agent + chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3) # Remove the last dim + + with th.no_grad(): + mac_out_detach = mac_out.clone().detach() + mac_out_detach[avail_actions == 0] = -9999999 + max_action_qvals, cur_max_actions = mac_out_detach.max(dim=3, keepdim=True) # [bs, traj, n_agent, 1] + + # %%%%%%%%%%%%%%%%%%%%%%%%%% Calculate the Q-Values necessary for the target %%%%%%%%%%%%%%%%%%%%%%%%%% + # Set target mac to testing mode + self.target_mac.set_evaluation_mode() + target_mac_out = [] + self.target_mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + target_agent_outs = self.target_mac.forward(batch, t=t) + target_mac_out.append(target_agent_outs) + + # We don't need the first timesteps Q-Value estimate for calculating targets + target_mac_out = th.stack(target_mac_out, dim=1) # Concat across time + + # Max over target Q-Values + assert self.args.double_q + # Get actions that maximise live Q (for double q-learning) + target_chosen_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3) + + # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Mixer %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + # Set mixing net to training mode + mixer.train() + # %%%%%%%%%%%%%%%%%%%%%%% Current \sum_i{Q_i} %%%%%%%%%%%%%%%%%%%%%%%% + ans_chosen = mixer(chosen_action_qvals, batch["state"][:, :-1], is_v=True) + # %%%%%%%%%%%%%%%%%%%%%%% Current \sum_i{adv_i} %%%%%%%%%%%%%%%%%%%%%%%% + ans_adv = mixer(chosen_action_qvals, batch["state"][:, :-1], actions=actions_onehot, + max_q_i=max_action_qvals[:, :-1].contiguous().squeeze(3), is_v=False) + + chosen_action_qvals = ans_chosen + ans_adv + + with th.no_grad(): + self.target_mixer.eval() + # %%%%%%%%%%%%%%%%%%%%%%% Target \sum_i{Q_i} %%%%%%%%%%%%%%%%%%%%%%%% + target_chosen = self.target_mixer(target_chosen_qvals, batch["state"], is_v=True) + + # %%%%%%%%%%%%%%%%%%%%%%% Target \sum_i{adv_i} %%%%%%%%%%%%%%%%%%%%%%%% + # Mask out unavailable actions + target_mac_out[avail_actions == 0] = -9999999 + target_max_qvals = target_mac_out.max(dim=3)[0] + # Onehot target actions + cur_max_actions_onehot = th.zeros(cur_max_actions.squeeze(3).shape + (self.n_actions,), + device=cur_max_actions.device) + cur_max_actions_onehot = cur_max_actions_onehot.scatter_(3, cur_max_actions, 1) + target_adv = self.target_mixer(target_chosen_qvals, batch["state"], + actions=cur_max_actions_onehot, max_q_i=target_max_qvals, is_v=False) + + target_max_qvals = target_chosen + target_adv + + # Calculate 1-step Q-Learning targets + targets = build_td_lambda_targets(rewards, terminated, mask, target_max_qvals, + self.args.gamma, self.args.td_lambda) + + # Td-error + td_error = (chosen_action_qvals - targets.detach()) + + mask = mask.expand_as(td_error) + + # 0-out the targets that came from padded data + masked_td_error = td_error * mask + + # Normal L2 loss, take mean over actual data + loss = 0.5 * (masked_td_error ** 2).sum() / mask.sum() + + # Optimise + if not split_data: + optimiser.zero_grad() + + loss.backward() + + if not split_data: + grad_norm = th.nn.utils.clip_grad_norm_(params, self.args.grad_norm_clip) + optimiser.step() + + self.train_t += 1 + self.avg_time += (time.time() - start_time - self.avg_time) / self.train_t + print("Avg cost {} seconds".format(self.avg_time)) + + if not split_data and t_env - self.log_stats_t >= self.args.learner_log_interval: + with th.no_grad(): + is_max_action = (cur_max_actions[:, :-1] == actions).int().float() + masked_hit_prob = th.mean(is_max_action, dim=2) * mask + hit_prob = masked_hit_prob.sum() / mask.sum() + self.logger.log_stat("loss", loss.item(), t_env) + self.logger.log_stat("hit_prob", hit_prob.item(), t_env) + self.logger.log_stat("grad_norm", grad_norm, t_env) + mask_elems = mask.sum().item() + self.logger.log_stat("td_error_abs", (masked_td_error.abs().sum().item() / mask_elems), t_env) + self.logger.log_stat("q_taken_mean", + (chosen_action_qvals * mask).sum().item() / (mask_elems * self.args.n_agents), + t_env) + self.logger.log_stat("target_mean", (targets * mask).sum().item() / (mask_elems * self.args.n_agents), + t_env) + self.log_stats_t = t_env + + def train(self, batch: EpisodeBatch, t_env: int, episode_num: int, save_data=None): + if self.args.use_cuda and str(self.mac.get_device()) == "cpu": + self.mac.cuda() + + if self.args.n_agents > 20: + split_num = 2 + a, b, c, d = batch.split(split_num) + + # Optimise + self.optimiser.zero_grad() + + self.sub_train(a, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params, + save_data=save_data, split_data=True) + del a + + self.sub_train(b, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params, + save_data=save_data, split_data=True) + del b + + self.sub_train(c, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params, + save_data=save_data, split_data=True) + del c + + self.sub_train(d, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params, + save_data=save_data, split_data=True) + del d + + # Optimise + grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip) + self.optimiser.step() + + else: + self.sub_train(batch, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params, + save_data=save_data, split_data=False) + + if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0: + self._update_targets() + self.last_target_update_episode = episode_num + + def _update_targets(self): + self.target_mac.load_state(self.mac) + if self.mixer is not None: + self.target_mixer.load_state_dict(self.mixer.state_dict()) + self.logger.console_logger.info("Updated target network") + + def cuda(self): + self.mac.cuda() + self.target_mac.cuda() + if self.mixer is not None: + self.mixer.cuda() + self.target_mixer.cuda() + + def save_models(self, path): + self.mac.save_models(path) + if self.mixer is not None: + th.save(self.mixer.state_dict(), "{}/mixer.th".format(path)) + th.save(self.optimiser.state_dict(), "{}/opt.th".format(path)) + + def load_models(self, path): + self.mac.load_models(path) + # Not quite right but I don't want to save target networks + self.target_mac.load_models(path) + if self.mixer is not None: + self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage)) + self.target_mixer.load_state_dict(th.load("{}/mixer.th".format(path), + map_location=lambda storage, loc: storage)) + self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage)) diff --git a/src/learners/nq_learner.py b/src/learners/nq_learner.py new file mode 100644 index 0000000..3eb67b2 --- /dev/null +++ b/src/learners/nq_learner.py @@ -0,0 +1,234 @@ +import copy +import time + +import torch as th +from torch.optim import RMSprop, Adam + +from components.episode_buffer import EpisodeBatch +from modules.mixers.nmix import Mixer +from modules.mixers.qatten import QattenMixer +from modules.mixers.vdn import VDNMixer +from utils.rl_utils import build_td_lambda_targets, build_q_lambda_targets +from utils.th_utils import get_parameters_num + + +def calculate_target_q(target_mac, batch, enable_parallel_computing=False, thread_num=4): + if enable_parallel_computing: + th.set_num_threads(thread_num) + with th.no_grad(): + # Set target mac to testing mode + target_mac.set_evaluation_mode() + target_mac_out = [] + target_mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + target_agent_outs = target_mac.forward(batch, t=t) + target_mac_out.append(target_agent_outs) + + # We don't need the first timesteps Q-Value estimate for calculating targets + target_mac_out = th.stack(target_mac_out, dim=1) # Concat across time + return target_mac_out + + +def calculate_n_step_td_target(target_mixer, target_max_qvals, batch, rewards, terminated, mask, gamma, td_lambda, + enable_parallel_computing=False, thread_num=4, q_lambda=False, target_mac_out=None): + if enable_parallel_computing: + th.set_num_threads(thread_num) + + with th.no_grad(): + # Set target mixing net to testing mode + target_mixer.eval() + # Calculate n-step Q-Learning targets + target_max_qvals = target_mixer(target_max_qvals, batch["state"]) + + if q_lambda: + raise NotImplementedError + qvals = th.gather(target_mac_out, 3, batch["actions"]).squeeze(3) + qvals = target_mixer(qvals, batch["state"]) + targets = build_q_lambda_targets(rewards, terminated, mask, target_max_qvals, qvals, gamma, td_lambda) + else: + targets = build_td_lambda_targets(rewards, terminated, mask, target_max_qvals, gamma, td_lambda) + return targets.detach() + + +class NQLearner: + def __init__(self, mac, scheme, logger, args): + self.args = args + self.mac = mac + self.logger = logger + + self.last_target_update_episode = 0 + self.device = th.device('cuda' if args.use_cuda else 'cpu') + self.params = list(mac.parameters()) + + if args.mixer == "qatten": + self.mixer = QattenMixer(args) + elif args.mixer == "vdn": + self.mixer = VDNMixer() + elif args.mixer == "qmix": # 31.521K + self.mixer = Mixer(args) + else: + raise "mixer error" + + self.target_mixer = copy.deepcopy(self.mixer) + self.params += list(self.mixer.parameters()) + + # print('Mixer Size: ') + # print(get_parameters_num(self.mixer.parameters())) + self.logger.console_logger.info('Mixer Size: {}'.format(get_parameters_num(self.mixer.parameters()))) + + if self.args.optimizer == 'adam': + self.optimiser = Adam(params=self.params, lr=args.lr, weight_decay=getattr(args, "weight_decay", 0)) + else: + self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps) + + # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC + self.target_mac = copy.deepcopy(mac) + self.log_stats_t = -self.args.learner_log_interval - 1 + self.train_t = 0 + self.avg_time = 0 + + self.enable_parallel_computing = (not self.args.use_cuda) and getattr(self.args, 'enable_parallel_computing', + False) + # self.enable_parallel_computing = False + if self.enable_parallel_computing: + from multiprocessing import Pool + # Multiprocessing pool for parallel computing. + self.pool = Pool(1) + + def train(self, batch: EpisodeBatch, t_env: int, episode_num: int): + start_time = time.time() + if self.args.use_cuda and str(self.mac.get_device()) == "cpu": + self.mac.cuda() + + # Get the relevant quantities + rewards = batch["reward"][:, :-1] + actions = batch["actions"][:, :-1] + terminated = batch["terminated"][:, :-1].float() + mask = batch["filled"][:, :-1].float() + mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1]) + avail_actions = batch["avail_actions"] + + if self.enable_parallel_computing: + target_mac_out = self.pool.apply_async( + calculate_target_q, + (self.target_mac, batch, True, self.args.thread_num) + ) + + # Calculate estimated Q-Values + self.mac.set_train_mode() + mac_out = [] + self.mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + agent_outs = self.mac.forward(batch, t=t) + mac_out.append(agent_outs) + mac_out = th.stack(mac_out, dim=1) # Concat over time + # TODO: double DQN action, COMMENT: do not need copy + mac_out[avail_actions == 0] = -9999999 + # Pick the Q-Values for the actions taken by each agent + chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3) # Remove the last dim + + # Calculate the Q-Values necessary for the target + with th.no_grad(): + if self.enable_parallel_computing: + target_mac_out = target_mac_out.get() + else: + target_mac_out = calculate_target_q(self.target_mac, batch) + + # Max over target Q-Values/ Double q learning + # mac_out_detach = mac_out.clone().detach() + # TODO: COMMENT: do not need copy + mac_out_detach = mac_out + # mac_out_detach[avail_actions == 0] = -9999999 + cur_max_actions = mac_out_detach.max(dim=3, keepdim=True)[1] + + target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3) + + assert getattr(self.args, 'q_lambda', False) == False + if self.args.mixer.find("qmix") != -1 and self.enable_parallel_computing: + targets = self.pool.apply_async( + calculate_n_step_td_target, + (self.target_mixer, target_max_qvals, batch, rewards, terminated, mask, self.args.gamma, + self.args.td_lambda, True, self.args.thread_num, False, None) + ) + else: + targets = calculate_n_step_td_target( + self.target_mixer, target_max_qvals, batch, rewards, terminated, mask, self.args.gamma, + self.args.td_lambda + ) + + # Set mixing net to training mode + self.mixer.train() + # Mixer + chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1]) + + if self.args.mixer.find("qmix") != -1 and self.enable_parallel_computing: + targets = targets.get() + + td_error = (chosen_action_qvals - targets) + td_error2 = 0.5 * td_error.pow(2) + + mask = mask.expand_as(td_error2) + masked_td_error = td_error2 * mask + + mask_elems = mask.sum() + loss = masked_td_error.sum() / mask_elems + + # Optimise + self.optimiser.zero_grad() + loss.backward() + grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip) + self.optimiser.step() + + self.train_t += 1 + self.avg_time += (time.time() - start_time - self.avg_time) / self.train_t + #print("Avg cost {} seconds".format(self.avg_time)) + self.logger.console_logger.info("Avg cost {} seconds".format(self.avg_time)) + + if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0: + self._update_targets() + self.last_target_update_episode = episode_num + + if t_env - self.log_stats_t >= self.args.learner_log_interval: + # For log + with th.no_grad(): + mask_elems = mask_elems.item() + td_error_abs = masked_td_error.abs().sum().item() / mask_elems + q_taken_mean = (chosen_action_qvals * mask).sum().item() / (mask_elems * self.args.n_agents) + target_mean = (targets * mask).sum().item() / (mask_elems * self.args.n_agents) + self.logger.log_stat("loss_td", loss.item(), t_env) + self.logger.log_stat("grad_norm", grad_norm, t_env) + self.logger.log_stat("td_error_abs", td_error_abs, t_env) + self.logger.log_stat("q_taken_mean", q_taken_mean, t_env) + self.logger.log_stat("target_mean", target_mean, t_env) + self.log_stats_t = t_env + + def _update_targets(self): + self.target_mac.load_state(self.mac) + if self.mixer is not None: + self.target_mixer.load_state_dict(self.mixer.state_dict()) + self.logger.console_logger.info("Updated target network") + + def cuda(self): + self.mac.cuda() + self.target_mac.cuda() + if self.mixer is not None: + self.mixer.cuda() + self.target_mixer.cuda() + + def save_models(self, path): + self.mac.save_models(path) + if self.mixer is not None: + th.save(self.mixer.state_dict(), "{}/mixer.th".format(path)) + th.save(self.optimiser.state_dict(), "{}/opt.th".format(path)) + + def load_models(self, path): + self.mac.load_models(path) + # Not quite right but I don't want to save target networks + self.target_mac.load_models(path) + if self.mixer is not None: + self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage)) + self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage)) + + def __del__(self): + if self.enable_parallel_computing: + self.pool.close() diff --git a/src/learners/nq_learner_data_augmentation.py b/src/learners/nq_learner_data_augmentation.py new file mode 100644 index 0000000..c52640b --- /dev/null +++ b/src/learners/nq_learner_data_augmentation.py @@ -0,0 +1,243 @@ +import copy +import time + +import torch as th +from multiprocessing import Pool +from torch.optim import RMSprop, Adam + +from components.episode_buffer import EpisodeBatch +from modules.mixers.nmix import Mixer +from modules.mixers.qatten import QattenMixer +from modules.mixers.vdn import VDNMixer +from utils.rl_utils import build_td_lambda_targets, build_q_lambda_targets +from utils.th_utils import get_parameters_num +from utils.data_processing import do_data_augmentation + + +def calculate_target_q(target_mac, batch, enable_parallel_computing=False, thread_num=4): + if enable_parallel_computing: + th.set_num_threads(thread_num) + with th.no_grad(): + # Set target mac to testing mode + # target_mac.set_evaluation_mode() + target_mac_out = [] + target_mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + target_agent_outs = target_mac.forward(batch, t=t) + target_mac_out.append(target_agent_outs) + + # We don't need the first timesteps Q-Value estimate for calculating targets + target_mac_out = th.stack(target_mac_out, dim=1) # Concat across time + return target_mac_out + + +def calculate_n_step_td_target(target_mixer, target_max_qvals, batch, rewards, terminated, mask, gamma, td_lambda, + enable_parallel_computing=False, thread_num=4, q_lambda=False, target_mac_out=None): + if enable_parallel_computing: + th.set_num_threads(thread_num) + + with th.no_grad(): + # Set target mixing net to testing mode + target_mixer.eval() + # Calculate n-step Q-Learning targets + target_max_qvals = target_mixer(target_max_qvals, batch["state"]) + + if q_lambda: + qvals = th.gather(target_mac_out, 3, batch["actions"]).squeeze(3) + qvals = target_mixer(qvals, batch["state"]) + targets = build_q_lambda_targets(rewards, terminated, mask, target_max_qvals, qvals, gamma, td_lambda) + else: + targets = build_td_lambda_targets(rewards, terminated, mask, target_max_qvals, gamma, td_lambda) + return targets.detach() + + +class NQLearnerDataAugmentation: + def __init__(self, mac, scheme, logger, args): + self.args = args + self.mac = mac + self.logger = logger + + self.last_target_update_episode = 0 + self.device = th.device('cuda' if args.use_cuda else 'cpu') + self.params = list(mac.parameters()) + + if args.mixer == "qatten": + self.mixer = QattenMixer(args) + elif args.mixer == "vdn": + self.mixer = VDNMixer() + elif args.mixer == "qmix": # 31.521K + self.mixer = Mixer(args) + else: + raise "mixer error" + + self.target_mixer = copy.deepcopy(self.mixer) + self.params += list(self.mixer.parameters()) + + print('Mixer Size: ') + print(get_parameters_num(self.mixer.parameters())) + + if self.args.optimizer == 'adam': + self.optimiser = Adam(params=self.params, lr=args.lr, weight_decay=getattr(args, "weight_decay", 0)) + else: + self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps) + + # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC + self.target_mac = copy.deepcopy(mac) + self.log_stats_t = -self.args.learner_log_interval - 1 + self.train_t = 0 + self.avg_time = 0 + + self.enable_parallel_computing = (not self.args.use_cuda) and getattr(self.args, 'enable_parallel_computing', + True) + # self.enable_parallel_computing = False + if self.enable_parallel_computing: + # Multiprocessing pool for parallel computing. + # ctx = th.multiprocessing.get_context("spawn") + # self.pool = ctx.Pool() + self.pool = Pool(1) + + def train_each_batch(self, batch): + # Get the relevant quantities + rewards = batch["reward"][:, :-1] + actions = batch["actions"][:, :-1] + terminated = batch["terminated"][:, :-1].float() + mask = batch["filled"][:, :-1].float() + mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1]) + avail_actions = batch["avail_actions"] + + if self.enable_parallel_computing: + target_mac_out = self.pool.apply_async( + calculate_target_q, + (self.target_mac, batch, True, self.args.thread_num) + ) + + # Calculate estimated Q-Values + self.mac.set_train_mode() + mac_out = [] + self.mac.init_hidden(batch.batch_size) + for t in range(batch.max_seq_length): + agent_outs = self.mac.forward(batch, t=t) + mac_out.append(agent_outs) + mac_out = th.stack(mac_out, dim=1) # Concat over time + + # Pick the Q-Values for the actions taken by each agent + chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3) # Remove the last dim + + # Calculate the Q-Values necessary for the target + with th.no_grad(): + if self.enable_parallel_computing: + target_mac_out = target_mac_out.get() + else: + target_mac_out = calculate_target_q(self.target_mac, batch) + + # Max over target Q-Values/ Double q learning + mac_out_detach = mac_out.clone().detach() + mac_out_detach[avail_actions == 0] = -9999999 + cur_max_actions = mac_out_detach.max(dim=3, keepdim=True)[1] + target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3) + + assert getattr(self.args, 'q_lambda', False) == False + if self.args.mixer.find("qmix") != -1 and self.enable_parallel_computing: + targets = self.pool.apply_async( + calculate_n_step_td_target, + (self.target_mixer, target_max_qvals, batch, rewards, terminated, mask, self.args.gamma, + self.args.td_lambda, True, self.args.thread_num, False, None) + ) + else: + targets = calculate_n_step_td_target( + self.target_mixer, target_max_qvals, batch, rewards, terminated, mask, self.args.gamma, + self.args.td_lambda + ) + + # Set mixing net to training mode + self.mixer.train() + # Mixer + chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1]) + + if self.args.mixer.find("qmix") != -1 and self.enable_parallel_computing: + targets = targets.get() + + td_error = (chosen_action_qvals - targets) + td_error2 = 0.5 * td_error.pow(2) + + mask = mask.expand_as(td_error2) + masked_td_error = td_error2 * mask + + mask_elems = mask.sum() + loss = masked_td_error.sum() / mask_elems + + # Optimise + self.optimiser.zero_grad() + loss.backward() + # Optimise + grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip) + self.optimiser.step() + + # For log + with th.no_grad(): + td_error_abs = (masked_td_error.abs().sum() / mask_elems).item() + q_taken_mean = (chosen_action_qvals * mask).sum() / (mask_elems * self.args.n_agents).item() + target_mean = (targets * mask).sum() / (mask_elems * self.args.n_agents).item() + return loss, td_error_abs, q_taken_mean, target_mean, grad_norm + + def train(self, batch: EpisodeBatch, t_env: int, episode_num: int): + start_time = time.time() + if self.args.use_cuda and str(self.mac.get_device()) == "cpu": + self.mac.cuda() + + # Do data augmentation here. + for data_augmentation_time in range(self.args.augment_times): + new_batch = do_data_augmentation(self.args, batch, augment_times=1) + # print(new_batch.batch_size) + loss, td_error_abs, q_taken_mean, target_mean, grad_norm = self.train_each_batch(new_batch) + del new_batch + + loss, td_error_abs, q_taken_mean, target_mean, grad_norm = self.train_each_batch(batch) + + self.train_t += 1 + self.avg_time += (time.time() - start_time - self.avg_time) / self.train_t + print("Avg cost {} seconds".format(self.avg_time)) + + if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0: + self._update_targets() + self.last_target_update_episode = episode_num + + if t_env - self.log_stats_t >= self.args.learner_log_interval: + self.logger.log_stat("loss_td", loss, t_env) + self.logger.log_stat("grad_norm", grad_norm, t_env) + + self.logger.log_stat("td_error_abs", td_error_abs, t_env) + self.logger.log_stat("q_taken_mean", q_taken_mean, t_env) + self.logger.log_stat("target_mean", target_mean, t_env) + self.log_stats_t = t_env + + def _update_targets(self): + self.target_mac.load_state(self.mac) + if self.mixer is not None: + self.target_mixer.load_state_dict(self.mixer.state_dict()) + self.logger.console_logger.info("Updated target network") + + def cuda(self): + self.mac.cuda() + self.target_mac.cuda() + if self.mixer is not None: + self.mixer.cuda() + self.target_mixer.cuda() + + def save_models(self, path): + self.mac.save_models(path) + if self.mixer is not None: + th.save(self.mixer.state_dict(), "{}/mixer.th".format(path)) + th.save(self.optimiser.state_dict(), "{}/opt.th".format(path)) + + def load_models(self, path): + self.mac.load_models(path) + # Not quite right but I don't want to save target networks + self.target_mac.load_models(path) + if self.mixer is not None: + self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage)) + self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage)) + + def __del__(self): + if self.enable_parallel_computing: + self.pool.close() diff --git a/src/main.py b/src/main.py new file mode 100644 index 0000000..199f3a0 --- /dev/null +++ b/src/main.py @@ -0,0 +1,124 @@ +import random + +import numpy as np +import os +import collections +from os.path import dirname, abspath, join +from copy import deepcopy +from sacred import Experiment, SETTINGS +from sacred.observers import FileStorageObserver +from sacred.utils import apply_backspaces_and_linefeeds +import sys +import torch as th +from utils.logging import get_logger +import yaml +import collections.abc + +from run import REGISTRY as run_REGISTRY + +SETTINGS['CAPTURE_MODE'] = "no" # set to "no" if you want to see stdout/stderr in console "fd" or "no" +logger = get_logger() + +ex = Experiment("pymarl") +ex.logger = logger +ex.captured_out_filter = apply_backspaces_and_linefeeds + +results_path = join(dirname(dirname(abspath(__file__)))) + + +@ex.main +def my_main(_run, _config, _log): + # Setting the random seed throughout the modules + config = config_copy(_config) + random.seed(config["seed"]) + np.random.seed(config["seed"]) + th.manual_seed(config["seed"]) + th.cuda.manual_seed(config["seed"]) + # th.cuda.manual_seed_all(config["seed"]) + th.backends.cudnn.deterministic = True # cudnn + + + config['env_args']['seed'] = config["seed"] + + # run + run_REGISTRY[_config['run']](_run, config, _log) + + +def _get_config(params, arg_name, subfolder): + config_name = None + for _i, _v in enumerate(params): + if _v.split("=")[0] == arg_name: + config_name = _v.split("=")[1] + del params[_i] + break + + if config_name is not None: + with open(os.path.join(os.path.dirname(__file__), "config", subfolder, "{}.yaml".format(config_name)), + "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "{}.yaml error: {}".format(config_name, exc) + return config_dict + + +def recursive_dict_update(d, u): + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = recursive_dict_update(d.get(k, {}), v) + else: + d[k] = v + return d + + +def config_copy(config): + if isinstance(config, dict): + return {k: config_copy(v) for k, v in config.items()} + elif isinstance(config, list): + return [config_copy(v) for v in config] + else: + return deepcopy(config) + + +def parse_command(params, key, default): + result = default + for _i, _v in enumerate(params): + if _v.split("=")[0].strip() == key: + result = _v[_v.index('=') + 1:].strip() + break + return result + + +if __name__ == '__main__': + params = deepcopy(sys.argv) + + # Get the defaults from default.yaml + with open(os.path.join(os.path.dirname(__file__), "config", "default.yaml"), "r") as f: + try: + config_dict = yaml.load(f, Loader=yaml.SafeLoader) + except yaml.YAMLError as exc: + assert False, "default.yaml error: {}".format(exc) + + # Load algorithm and env base configs + env_config = _get_config(params, "--env-config", "envs") + alg_config = _get_config(params, "--config", "algs") + # config_dict = {**config_dict, **env_config, **alg_config} + config_dict = recursive_dict_update(config_dict, env_config) + config_dict = recursive_dict_update(config_dict, alg_config) + + # now add all the config to sacred + ex.add_config(config_dict) + + # Save to disk by default for sacred + map_name = parse_command(params, "env_args.map_name", config_dict['env_args']['map_name']) + algo_name = parse_command(params, "name", config_dict['name']) + local_results_path = parse_command(params, "local_results_path", config_dict['local_results_path']) + file_obs_path = join(results_path, local_results_path, "sacred", map_name, algo_name) + + logger.info("Saving to FileStorageObserver in {}.".format(file_obs_path)) + ex.observers.append(FileStorageObserver.create(file_obs_path)) + + ex.run_commandline(params) + + # flush + sys.stdout.flush() diff --git a/src/modules/__init__.py b/src/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/modules/agents/FeUdal_agent.py b/src/modules/agents/FeUdal_agent.py new file mode 100644 index 0000000..0295c55 --- /dev/null +++ b/src/modules/agents/FeUdal_agent.py @@ -0,0 +1,92 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch + +class Feudal_ManagerAgent(nn.Module): + def __init__(self, input_shape, args): + super(Feudal_ManagerAgent, self).__init__() + self.args = args + + # Manager network + self.manager_fc1 = nn.Linear(input_shape, args.manager_hidden_dim) + #self.manager_rnn = nn.GRUCell(args.manager_hidden_dim, args.manager_hidden_dim) + self.manager_rnn = nn.LSTMCell(args.manager_hidden_dim, args.manager_hidden_dim) + self.manager_fc2 = nn.Linear(args.manager_hidden_dim, args.state_dim) + + # 目標生成 + self.goal_network = nn.Linear(args.manager_hidden_dim, args.state_dim) + + # 狀態值估計 V_t^M + self.value_network = nn.Linear(args.manager_hidden_dim, 1) + + def init_hidden(self): + # Initialize hidden states for both manager and worker + manager_hidden = self.manager_fc1.weight.new(1, self.args.manager_hidden_dim).zero_() + manager_cell = self.manager_fc1.weight.new(1, self.args.manager_hidden_dim).zero_() + return (manager_hidden, manager_cell) + + def forward(self, inputs, hidden): + x = F.relu(self.manager_fc1(inputs)) + h_in, c_in = hidden + h_in = h_in.reshape(-1, self.args.manager_hidden_dim) + c_in = c_in.reshape(-1, self.args.manager_hidden_dim) + h, c = self.manager_rnn(x, (h_in, c_in)) + + # 生成目標 + goal = self.goal_network(h) + + # 估計狀態值 + value = self.value_network(h) + + return goal, value, (h, c) + +class Feudal_WorkerAgent(nn.Module): + def __init__(self, input_shape, args): + super(Feudal_WorkerAgent, self).__init__() + self.args = args + + # Worker 網絡 + self.worker_fc1 = nn.Linear(input_shape, args.worker_hidden_dim) + self.worker_rnn = nn.LSTMCell(args.worker_hidden_dim, args.worker_hidden_dim) + + # U_t: Action embedding matrix (1x16) + self.U_embedding = nn.Linear(args.worker_hidden_dim, args.embedding_dim) + + # w_t: 優勢方向/權重 (1x16) + self.w_network = nn.Linear(args.state_dim, args.embedding_dim) + + # 最終的 Q 值輸出 + self.q_network = nn.Linear(args.embedding_dim, args.n_actions) + + def init_hidden(self): + # Initialize hidden states for both manager and worker + worker_hidden = self.worker_fc1.weight.new(1, self.args.worker_hidden_dim).zero_() + worker_cell = self.worker_fc1.weight.new(1, self.args.worker_hidden_dim).zero_() + return (worker_hidden, worker_cell) + + def forward(self, inputs, worker_hidden, single_past_goals, batch_past_goals, goal): + # Worker RNN + x = F.relu(self.worker_fc1(inputs)) + h_in, c_in = worker_hidden + h_in = h_in.reshape(-1, self.args.worker_hidden_dim) + c_in = c_in.reshape(-1, self.args.worker_hidden_dim) + h, c = self.worker_rnn(x, (h_in, c_in)) + + # 生成 U_t (action embedding matrix) + U_t = self.U_embedding(h) + + # 生成 w_t (優勢方向) + w_t = self.w_network(goal) + + # 計算 s_t (weighted state) + s_t = U_t * w_t # element-wise multiplication + + # 生成 Q 值 + q = self.q_network(s_t) + + return q, (h, c), single_past_goals, batch_past_goals + + + + + diff --git a/src/modules/agents/__init__.py b/src/modules/agents/__init__.py new file mode 100644 index 0000000..ee9ac85 --- /dev/null +++ b/src/modules/agents/__init__.py @@ -0,0 +1,23 @@ +REGISTRY = {} + +from .hpn_rnn_agent import HPN_RNNAgent +from .hpns_rnn_agent import HPNS_RNNAgent +from .asn_rnn_agent import AsnRNNAgent +from .deepset_hyper_rnn_agent import DeepSetHyperRNNAgent +from .deepset_rnn_agent import DeepSetRNNAgent +from .gnn_rnn_agent import GnnRNNAgent +from .n_rnn_agent import NRNNAgent +from .rnn_agent import RNNAgent +from .updet_agent import UPDeT +from .FeUdal_agent import Feudal_ManagerAgent + +REGISTRY["rnn"] = RNNAgent +REGISTRY["n_rnn"] = NRNNAgent +REGISTRY["hpn_rnn"] = HPN_RNNAgent +REGISTRY["hpns_rnn"] = HPNS_RNNAgent +REGISTRY["deepset_rnn"] = DeepSetRNNAgent +REGISTRY["deepset_hyper_rnn"] = DeepSetHyperRNNAgent +REGISTRY["updet_agent"] = UPDeT +REGISTRY["asn_rnn"] = AsnRNNAgent +REGISTRY["gnn_rnn"] = GnnRNNAgent +REGISTRY["feudal_agent"] = Feudal_ManagerAgent diff --git a/src/modules/agents/asn_rnn_agent.py b/src/modules/agents/asn_rnn_agent.py new file mode 100644 index 0000000..cb307cd --- /dev/null +++ b/src/modules/agents/asn_rnn_agent.py @@ -0,0 +1,77 @@ +import json + +import torch as th +import torch.nn as nn +import torch.nn.functional as F + +def read_json(path): + with open(path, 'rb') as f: + data = json.load(f) + return data + + +class AsnRNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(AsnRNNAgent, self).__init__() + self.args = args + + print('#' * 18) + print('using asn') + print(args) + print('#' * 18) + + self.map_name = args.env_args['map_name'] + '_obs' + # config path + map_config = read_json('./obs_config.json') + + assert self.map_name in map_config.keys(), 'map config not find' + assert input_shape == map_config[self.map_name]['model_input_size'], 'input shape mismatch' + + self.enemies_feat_start = map_config[self.map_name]['model_input_compose']['0']['size'] + self.enemies_num, self.enemy_feats_size = map_config[self.map_name]['model_input_compose']['1']['size'] + + # network struct + self.env_info_fc1 = nn.Linear(input_shape, args.asn_hidden_size) + self.env_info_fc2 = nn.Linear(args.asn_hidden_size, args.asn_hidden_size) + self.env_info_rnn3 = nn.GRUCell(args.asn_hidden_size, args.asn_hidden_size) + + # no-op + stop + up, down, left, right + self.wo_action_fc = nn.Linear(args.asn_hidden_size, 6) + + + self.enemies_info_fc1 = nn.Linear(self.enemy_feats_size, args.asn_hidden_size) + self.enemies_info_fc2 = nn.Linear(args.asn_hidden_size, args.asn_hidden_size) + self.enemies_info_rnn3 = nn.GRUCell(args.asn_hidden_size, args.asn_hidden_size) + + def init_hidden(self): + # make hidden states on same device as model + return self.env_info_fc1.weight.new(1, self.args.asn_hidden_size * (1 + self.enemies_num)).zero_() + + def forward(self, inputs, hidden_state): + b, a, e = inputs.size() + inputs = inputs.view(-1, e) + + if hidden_state is not None: + hidden_state = hidden_state.reshape(-1, self.args.asn_hidden_size * (1 + self.enemies_num)) + + enemies_feats = [inputs[:, self.enemies_feat_start + i * self.enemy_feats_size: self.enemies_feat_start + self.enemy_feats_size * (1 + i)] for i in range(self.enemies_num)] + + h_in = th.split(hidden_state, self.args.asn_hidden_size, dim=-1) + h_in_env = h_in[0].reshape(-1, self.args.asn_hidden_size) + h_in_enemies = [_h.reshape(-1, self.args.asn_hidden_size) for _h in h_in[1:]] + + env_hidden_1 = F.relu(self.env_info_fc1(inputs)) + env_hidden_2 = self.env_info_fc2(env_hidden_1) + h_env = self.env_info_rnn3(env_hidden_2, h_in_env) + + wo_action_fc_Q = self.wo_action_fc(h_env) + + enemies_hiddent_1 = [F.relu(self.enemies_info_fc1(enemy_info)) for enemy_info in enemies_feats] + enemies_hiddent_2 = [self.enemies_info_fc2(enemy_info) for enemy_info in enemies_hiddent_1] + enemies_h_hiddent_3 = [self.enemies_info_rnn3(enemy_info, enemy_h) for enemy_info, enemy_h in zip(enemies_hiddent_2, h_in_enemies)] + + attack_enemy_id_Q = [th.sum(h_env * enemy_info, dim=-1, keepdim=True) for enemy_info in enemies_h_hiddent_3] + + q = th.cat([wo_action_fc_Q, *attack_enemy_id_Q], dim=-1) + hidden_state = th.cat([h_env, *enemies_h_hiddent_3], dim=-1) + return q.view(b, a, -1), hidden_state.view(b, a, -1) diff --git a/src/modules/agents/deepset_hyper_rnn_agent.py b/src/modules/agents/deepset_hyper_rnn_agent.py new file mode 100644 index 0000000..ef09b6a --- /dev/null +++ b/src/modules/agents/deepset_hyper_rnn_agent.py @@ -0,0 +1,100 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class DeepSetHyperRNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(DeepSetHyperRNNAgent, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_allies = args.n_allies + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + + # [4 + 1, (6, 5), (4, 5)], take 5m_vs_6m for example + self.own_feats_dim, self.enemy_feats_dim, self.ally_feats_dim = input_shape + self.enemy_feats_dim = self.enemy_feats_dim[-1] + self.ally_feats_dim = self.ally_feats_dim[-1] + + if self.args.obs_agent_id: + # embedding table for agent_id + self.agent_id_embedding = th.nn.Embedding(self.n_agents, self.args.rnn_hidden_dim) + + if self.args.obs_last_action: + # embedding table for action id + self.action_id_embedding = th.nn.Embedding(self.n_actions, self.args.rnn_hidden_dim) + + # Own features + self.fc1_own = nn.Linear(self.own_feats_dim, args.rnn_hidden_dim, bias=True) # only one bias is OK + + # Ally features + self.fc1_ally = nn.Linear(self.ally_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + + # Enemy features + self.fc1_enemy = nn.Linear(self.enemy_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + + self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim) + + self.fc2_normal_actions = nn.Linear(args.rnn_hidden_dim, args.output_normal_actions) # (no_op, stop, up, down, right, left) + # Multiple entities (use hyper net to process these features to ensure permutation invariant) + self.hyper_fc2_w_and_b_attack_actions = nn.Sequential( + nn.Linear(self.enemy_feats_dim, args.hpn_hyper_dim), + nn.ReLU(inplace=True), + nn.Linear(args.hpn_hyper_dim, args.rnn_hidden_dim * 1 + 1) + ) # output shape: rnn_hidden_dim * 1 + 1 + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1_own.weight.new(1, self.args.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state=None): + # [bs * n_agents, mv_fea_dim+own_fea_dim], [bs * n_agents * n_enemies, enemy_fea_dim], [bs * n_agents * n_allies, ally_fea_dim] + bs, own_feats_t, enemy_feats_t, ally_feats_t, embedding_indices = inputs + + # (1) Own feature + embedding_own = self.fc1_own(own_feats_t.reshape(-1, self.own_feats_dim)) + # (2) ID embeddings + if self.args.obs_agent_id: + agent_indices = embedding_indices[0] + embedding_own = embedding_own + self.agent_id_embedding(agent_indices).view(-1, self.args.rnn_hidden_dim) + if self.args.obs_last_action: + last_action_indices = embedding_indices[-1] + if last_action_indices is not None: # t != 0 + embedding_own = embedding_own + self.action_id_embedding(last_action_indices).view( + -1, self.args.rnn_hidden_dim) + + # (3) Enemy feature + embedding_enemies = self.fc1_enemy(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, rnn_hidden_dim] + embedding_enemies = embedding_enemies.sum(dim=1, keepdim=False) # [bs * n_agents, rnn_hidden_dim] + + # (4) Ally features + embedding_allies = self.fc1_ally(ally_feats_t).view( + bs * self.n_agents, self.n_allies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, rnn_hidden_dim] + embedding_allies = embedding_allies.sum(dim=1, keepdim=False) # [bs * n_agents, rnn_hidden_dim] + aggregated_embedding = embedding_own + embedding_enemies + embedding_allies # [bs * n_agents, rnn_hidden_dim] + + x = F.relu(aggregated_embedding, inplace=True) + if hidden_state is not None: + hidden_state = hidden_state.reshape(-1, self.args.rnn_hidden_dim) + h = self.rnn(x, hidden_state) + + # Q-values of normal actions + q_normal = self.fc2_normal_actions(h) # [bs * n_agents, 6] + + # Q-values of attack actions + fc2_w_and_b_attack = self.hyper_fc2_w_and_b_attack_actions(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies, self.args.rnn_hidden_dim + 1 + ).transpose(-2, -1) # [bs*n_agents, n_enemies, rnn_hidden_dim+1] -> [bs*n_agents, rnn_hidden_dim+1, n_enemies] + fc2_w_attack = fc2_w_and_b_attack[:, :-1] # [bs * n_agents, rnn_hidden_dim, n_enemies] + fc2_b_attack = fc2_w_and_b_attack[:, -1] # [bs * n_agents, n_enemies] + # [bs * n_agents, 1, rnn_hidden_dim] * [bs * n_agents, rnn_hidden_dim, n_enemies] = [bs * n_agents, 1, n_enemies] + q_attack = th.matmul(h.unsqueeze(1), fc2_w_attack).squeeze(1) + fc2_b_attack # [bs * n_agents, n_enemies] + + # Concat 2 types of Q-values + q = th.cat((q_normal, q_attack), dim=1) # [bs * n_agents, 6 + n_enemies] + + return q.view(bs, self.n_agents, -1), h.view(bs, self.n_agents, -1) \ No newline at end of file diff --git a/src/modules/agents/deepset_rnn_agent.py b/src/modules/agents/deepset_rnn_agent.py new file mode 100644 index 0000000..8e642ea --- /dev/null +++ b/src/modules/agents/deepset_rnn_agent.py @@ -0,0 +1,81 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class DeepSetRNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(DeepSetRNNAgent, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_allies = args.n_allies + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + + # [4 + 1, (6, 5), (4, 5)], take 5m_vs_6m for example + self.own_feats_dim, self.enemy_feats_dim, self.ally_feats_dim = input_shape + self.enemy_feats_dim = self.enemy_feats_dim[-1] + self.ally_feats_dim = self.ally_feats_dim[-1] + + if self.args.obs_agent_id: + # embedding table for agent_id + self.agent_id_embedding = th.nn.Embedding(self.n_agents, self.args.rnn_hidden_dim) + + if self.args.obs_last_action: + # embedding table for action id + self.action_id_embedding = th.nn.Embedding(self.n_actions, self.args.rnn_hidden_dim) + + # Own features + self.fc1_own = nn.Linear(self.own_feats_dim, args.rnn_hidden_dim, bias=True) # only one bias is OK + + # Ally features + self.fc1_ally = nn.Linear(self.ally_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + + # Enemy features + self.fc1_enemy = nn.Linear(self.enemy_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + + self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim) + self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions) + + # print(self.fc1_ally.weight.data.mean(), self.fc1_ally.weight.data.var()) + # print(self.fc1_enemy.weight.data.mean(), self.fc1_enemy.weight.data.var()) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1_own.weight.new(1, self.args.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state=None): + # [bs * n_agents, mv_fea_dim+own_fea_dim], [bs * n_agents * n_enemies, enemy_fea_dim], [bs * n_agents * n_allies, ally_fea_dim] + bs, own_feats_t, enemy_feats_t, ally_feats_t, embedding_indices = inputs + + # (1) Own feature + embedding_own = self.fc1_own(own_feats_t.reshape(-1, self.own_feats_dim)) + # (2) ID embeddings + if self.args.obs_agent_id: + agent_indices = embedding_indices[0] + embedding_own = embedding_own + self.agent_id_embedding(agent_indices).view(-1, self.args.rnn_hidden_dim) + if self.args.obs_last_action: + last_action_indices = embedding_indices[-1] + if last_action_indices is not None: # t != 0 + embedding_own = embedding_own + self.action_id_embedding(last_action_indices).view( + -1, self.args.rnn_hidden_dim) + + # (3) Enemy feature + embedding_enemies = self.fc1_enemy(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, rnn_hidden_dim] + embedding_enemies = embedding_enemies.sum(dim=1, keepdim=False) # [bs * n_agents, rnn_hidden_dim] + + # (4) Ally features + embedding_allies = self.fc1_ally(ally_feats_t).view( + bs * self.n_agents, self.n_allies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, rnn_hidden_dim] + embedding_allies = embedding_allies.sum(dim=1, keepdim=False) # [bs * n_agents, rnn_hidden_dim] + aggregated_embedding = embedding_own + embedding_enemies + embedding_allies # [bs * n_agents, rnn_hidden_dim] + + x = F.relu(aggregated_embedding, inplace=True) + if hidden_state is not None: + hidden_state = hidden_state.reshape(-1, self.args.rnn_hidden_dim) + h = self.rnn(x, hidden_state) + q = self.fc2(h) + return q.view(bs, self.n_agents, -1), h.view(bs, self.n_agents, -1) \ No newline at end of file diff --git a/src/modules/agents/gnn_rnn_agent.py b/src/modules/agents/gnn_rnn_agent.py new file mode 100644 index 0000000..21241ab --- /dev/null +++ b/src/modules/agents/gnn_rnn_agent.py @@ -0,0 +1,156 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class GraphConvLayer(nn.Module): + """Implements a GCN layer.""" + + def __init__(self, input_dim, output_dim, n_nodes): + super(GraphConvLayer, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.n_nodes = n_nodes + + self.lin_layer_neighbor = nn.Linear(input_dim, output_dim) + self.lin_layer_self = nn.Linear(input_dim, output_dim) + + def forward(self, inputs): + input_feature, adjacent_matrix = inputs + # [N, N] * [bs, N, fea_dim] + neighbors = th.matmul(adjacent_matrix, self.lin_layer_neighbor(input_feature)) # sum aggregation + neighbors = F.relu(neighbors, inplace=True) + + node_feats = self.lin_layer_self(input_feature) # node features + node_feats = F.relu(node_feats, inplace=True) + out = (node_feats + neighbors) / self.n_nodes # mean + return out + + def __repr__(self): + return self.__class__.__name__ + ' (' \ + + str(self.input_dim) + ' -> ' \ + + str(self.output_dim) + ')' + + +class GNN(nn.Module): + """ + A graph net that is used to pre-process input components, and solve the order issue. + gnn_rnn 35.404K for 5m_vs_6m + """ + + def __init__(self, fea_dim, n_nodes, hidden_size, layer_num=2, out_pool_type='avg'): + super(GNN, self).__init__() + self.fea_dim = fea_dim + self.n_nodes = n_nodes + self.hidden_size = hidden_size + self.layer_num = layer_num + self.out_pool_type = out_pool_type + + # Adjacent Matrix, assumes a fully connected graph. + self.register_buffer('adj', (th.ones(n_nodes, n_nodes) - th.eye(n_nodes))) + + # GNNs + GNN_layers = [] + previous_out_dim = fea_dim + for _ in range(self.layer_num): + GNN_layers.append(GraphConvLayer(input_dim=previous_out_dim, output_dim=hidden_size, n_nodes=n_nodes)) + previous_out_dim = hidden_size + self.gnn_layers = nn.Sequential(*GNN_layers) + + def forward(self, x): + # GNNs + out = self.gnn_layers([x, self.adj]) + + # Pooling + if self.out_pool_type == 'avg': + ret = out.mean(dim=1, keepdim=False) # Pooling over the node dimension. + elif self.out_pool_type == 'max': + ret, _ = out.max(dim=1, keepdim=False) + else: + raise NotImplementedError + return ret + + +class GnnRNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(GnnRNNAgent, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_allies = args.n_allies + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + + # [4 + 1, (6, 5), (4, 5)], take 5m_vs_6m for example + self.own_feats_dim, self.enemy_feats_dim, self.ally_feats_dim = input_shape + self.enemy_feats_dim = self.enemy_feats_dim[-1] + self.ally_feats_dim = self.ally_feats_dim[-1] + + # (1) To transform all kinds of features into the same dimension. + if self.args.obs_agent_id: + # embedding table for agent_id + self.agent_id_embedding = th.nn.Embedding(self.n_agents, self.args.rnn_hidden_dim) + if self.args.obs_last_action: + # embedding table for action id + self.action_id_embedding = th.nn.Embedding(self.n_actions, self.args.rnn_hidden_dim) + # Own features + self.fc1_own = nn.Linear(self.own_feats_dim, args.rnn_hidden_dim, bias=True) # only one bias is OK + # Ally features + self.fc1_ally = nn.Linear(self.ally_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + # Enemy features + self.fc1_enemy = nn.Linear(self.enemy_feats_dim, args.rnn_hidden_dim, bias=False) # only one bias is OK + + # (2) GNN + self.gnn = GNN(fea_dim=args.rnn_hidden_dim, n_nodes=self.n_agents + self.n_enemies, + hidden_size=args.rnn_hidden_dim, layer_num=args.gnn_layer_num, out_pool_type='avg') + + # (3) RNN and output + self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim) + self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions) + + # print(self.fc1_ally.weight.data.mean(), self.fc1_ally.weight.data.var()) + # print(self.fc1_enemy.weight.data.mean(), self.fc1_enemy.weight.data.var()) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1_own.weight.new(1, self.args.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state=None): + # [bs * n_agents, mv_fea_dim+own_fea_dim], [bs * n_agents * n_enemies, enemy_fea_dim], [bs * n_agents * n_allies, ally_fea_dim] + bs, own_feats_t, enemy_feats_t, ally_feats_t, embedding_indices = inputs + + # %%%%%%%%%% To transform all kinds of features into the same dimension. %%%%%%%%%% + # (1) Own feature + embedding_own = self.fc1_own(own_feats_t) # [bs * n_agents, rnn_hidden_dim] + # (2) ID embeddings + if self.args.obs_agent_id: + agent_indices = embedding_indices[0] + # [bs * n_agents, rnn_hidden_dim] + embedding_own = embedding_own + self.agent_id_embedding(agent_indices).view(-1, self.args.rnn_hidden_dim) + if self.args.obs_last_action: + last_action_indices = embedding_indices[-1] + if last_action_indices is not None: # t != 0 + # [bs * n_agents, rnn_hidden_dim] + embedding_own = embedding_own + self.action_id_embedding(last_action_indices).view( + -1, self.args.rnn_hidden_dim) + embedding_own = embedding_own.unsqueeze(dim=1) # [bs * n_agents, 1, rnn_hidden_dim] + # (3) Enemy feature + embedding_enemies = self.fc1_enemy(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, rnn_hidden_dim] + # (4) Ally features + embedding_allies = self.fc1_ally(ally_feats_t).view( + bs * self.n_agents, self.n_allies, self.args.rnn_hidden_dim + ) # [bs * n_agents, n_allies, rnn_hidden_dim] + + # [bs * n_agents, 1+n_allies+n_enemies, rnn_hidden_dim] + fea_embeddings = th.cat([embedding_own, embedding_allies, embedding_enemies], dim=1) + fea_embeddings = F.relu(fea_embeddings, inplace=True) + # %%%%%%%%%% To transform all kinds of features into the same dimension. %%%%%%%%%% + + x = self.gnn(fea_embeddings) # [bs * n_agents, rnn_hidden_dim] + + if hidden_state is not None: + hidden_state = hidden_state.reshape(-1, self.args.rnn_hidden_dim) + h = self.rnn(x, hidden_state) + q = self.fc2(h) + return q.view(bs, self.n_agents, -1), h.view(bs, self.n_agents, -1) diff --git a/src/modules/agents/hpn_rnn_agent.py b/src/modules/agents/hpn_rnn_agent.py new file mode 100644 index 0000000..e550b64 --- /dev/null +++ b/src/modules/agents/hpn_rnn_agent.py @@ -0,0 +1,244 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parameter import Parameter + + +def get_activation_func(name, hidden_dim): + """ + 'relu' + 'tanh' + 'leaky_relu' + 'elu' + 'prelu' + :param name: + :return: + """ + if name == "relu": + return nn.ReLU(inplace=True) + elif name == "tanh": + return nn.Tanh() + elif name == "leaky_relu": + return nn.LeakyReLU(negative_slope=0.01, inplace=True) + elif name == "elu": + return nn.ELU(alpha=1., inplace=True) + elif name == 'prelu': + return nn.PReLU(num_parameters=hidden_dim, init=0.25) + + +class Hypernet(nn.Module): + def __init__(self, input_dim, hidden_dim, main_input_dim, main_output_dim, activation_func, n_heads): + super(Hypernet, self).__init__() + + self.n_heads = n_heads + # the output dim of the hypernet + output_dim = main_input_dim * main_output_dim + # the output of the hypernet will be reshaped to [main_input_dim, main_output_dim] + self.main_input_dim = main_input_dim + self.main_output_dim = main_output_dim + + self.multihead_nn = nn.Sequential( + nn.Linear(input_dim, hidden_dim), + get_activation_func(activation_func, hidden_dim), + nn.Linear(hidden_dim, output_dim * self.n_heads), + ) + + def forward(self, x): + # [..., main_output_dim + main_output_dim + ... + main_output_dim] + # [bs, main_input_dim, n_heads * main_output_dim] + return self.multihead_nn(x).view([-1, self.main_input_dim, self.main_output_dim * self.n_heads]) + + +class Merger(nn.Module): + def __init__(self, head, fea_dim): + super(Merger, self).__init__() + self.head = head + if head > 1: + self.weight = Parameter(th.Tensor(1, head, fea_dim).fill_(1.)) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + """ + :param x: [bs, n_head, fea_dim] + :return: [bs, fea_dim] + """ + if self.head > 1: + return th.sum(self.softmax(self.weight) * x, dim=1, keepdim=False) + else: + return th.squeeze(x, dim=1) + + +class HPN_RNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(HPN_RNNAgent, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_allies = args.n_allies + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + self.n_heads = args.hpn_head_num + self.rnn_hidden_dim = args.rnn_hidden_dim + + # [4 + 1, (6, 5), (4, 5)] + self.own_feats_dim, self.enemy_feats_dim, self.ally_feats_dim = input_shape + self.enemy_feats_dim = self.enemy_feats_dim[-1] # [n_enemies, feat_dim] + self.ally_feats_dim = self.ally_feats_dim[-1] # [n_allies, feat_dim] + + if self.args.obs_agent_id: + # embedding table for agent_id + self.agent_id_embedding = th.nn.Embedding(self.n_agents, self.rnn_hidden_dim) + + if self.args.obs_last_action: + # embedding table for action id + self.action_id_embedding = th.nn.Embedding(self.n_actions, self.rnn_hidden_dim) + + # Unique Features (do not need hyper net) + self.fc1_own = nn.Linear(self.own_feats_dim, self.rnn_hidden_dim, bias=True) # only one bias is OK + + # %%%%%%%%%%%%%%%%%%%%%% Hypernet-based API input layer %%%%%%%%%%%%%%%%%%%% + # Multiple entities (use hyper net to process these features to ensure permutation invariant) + self.hyper_input_w_enemy = Hypernet( + input_dim=self.enemy_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=self.enemy_feats_dim, main_output_dim=self.rnn_hidden_dim, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: (enemy_feats_dim * self.rnn_hidden_dim) + self.hyper_input_w_ally = Hypernet( + input_dim=self.ally_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=self.ally_feats_dim, main_output_dim=self.rnn_hidden_dim, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: ally_feats_dim * rnn_hidden_dim + + # self.unify_input_heads = nn.Linear(self.rnn_hidden_dim * self.n_heads, self.rnn_hidden_dim) + self.unify_input_heads = Merger(self.n_heads, self.rnn_hidden_dim) + + self.rnn = nn.GRUCell(self.rnn_hidden_dim, self.rnn_hidden_dim) + + self.output_normal_actions = nn.Linear(self.rnn_hidden_dim, args.output_normal_actions) # (no_op, stop, up, down, right, left) + + # %%%%%%%%%%%%%%%%%%%%%% Hypernet-based APE output layer %%%%%%%%%%%%%%%%%%%% + # Multiple entities (use hyper net to process these features to ensure permutation invariant) + self.hyper_output_w_attack_action = Hypernet( + input_dim=self.enemy_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=self.rnn_hidden_dim, main_output_dim=1, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: rnn_hidden_dim * 1 + self.hyper_output_b_attack_action = Hypernet( + input_dim=self.enemy_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=1, main_output_dim=1, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: 1 + # self.unify_output_heads = nn.Linear(self.n_heads, 1) + self.unify_output_heads = Merger(self.n_heads, 1) + + if self.args.map_type == "MMM": + assert self.n_enemies >= self.n_agents, "For MMM map, for the reason that the 'attack' and 'rescue' use the same ids in SMAC, n_enemies must >= n_agents" + self.hyper_output_w_rescue_action = Hypernet( + input_dim=self.ally_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=self.rnn_hidden_dim, main_output_dim=1, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: rnn_hidden_dim * 1 + self.hyper_output_b_rescue_action = Hypernet( + input_dim=self.ally_feats_dim, hidden_dim=args.hpn_hyper_dim, + main_input_dim=1, main_output_dim=1, + activation_func=args.hpn_hyper_activation, n_heads=self.n_heads + ) # output shape: 1 + self.unify_rescue_output_heads = Merger(self.n_heads, 1) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1_own.weight.new(1, self.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state): + # [bs * n_agents, mv_fea_dim], [bs * n_agents * n_enemies, enemy_fea_dim], [bs * n_agents * n_allies, ally_fea_dim] + bs, own_feats_t, enemy_feats_t, ally_feats_t, embedding_indices = inputs + + # (1) Own feature + embedding_own = self.fc1_own(own_feats_t) # [bs * n_agents, rnn_hidden_dim] + + # (2) ID embeddings + if self.args.obs_agent_id: + agent_indices = embedding_indices[0] + # [bs * n_agents, rnn_hidden_dim * head] + embedding_own = embedding_own + self.agent_id_embedding(agent_indices).view( + -1, self.rnn_hidden_dim) + if self.args.obs_last_action: + last_action_indices = embedding_indices[-1] + if last_action_indices is not None: # t != 0 + # [bs * n_agents, rnn_hidden_dim * head] + embedding_own = embedding_own + self.action_id_embedding(last_action_indices).view( + -1, self.rnn_hidden_dim) + + # (3) Enemy feature: [bs * n_agents * n_enemies, enemy_fea_dim] -> [bs * n_agents * n_enemies, enemy_feats_dim, rnn_hidden_dim * n_heads] + input_w_enemy = self.hyper_input_w_enemy(enemy_feats_t) + # [bs * n_agents * n_enemies, 1, enemy_fea_dim] * [bs * n_agents * n_enemies, enemy_fea_dim, rnn_hidden_dim * head] = [bs * n_agents * n_enemies, 1, rnn_hidden_dim * head] + embedding_enemies = th.matmul(enemy_feats_t.unsqueeze(1), input_w_enemy).view( + bs * self.n_agents, self.n_enemies, self.n_heads, self.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, n_head, rnn_hidden_dim] + embedding_enemies = embedding_enemies.sum(dim=1, keepdim=False) # [bs * n_agents, n_head, rnn_hidden_dim] + + # (4) Ally features: [bs * n_agents * n_allies, ally_fea_dim] -> [bs * n_agents * n_allies, ally_feats_dim, rnn_hidden_dim * n_heads] + input_w_ally = self.hyper_input_w_ally(ally_feats_t) + # [bs * n_agents * n_allies, 1, ally_fea_dim] * [bs * n_agents * n_allies, ally_fea_dim, rnn_hidden_dim * head] = [bs * n_agents * n_allies, 1, rnn_hidden_dim * head] + embedding_allies = th.matmul(ally_feats_t.unsqueeze(1), input_w_ally).view( + bs * self.n_agents, self.n_allies, self.n_heads, self.rnn_hidden_dim + ) # [bs * n_agents, n_allies, n_head, rnn_hidden_dim] + embedding_allies = embedding_allies.sum(dim=1, keepdim=False) # [bs * n_agents, n_head, rnn_hidden_dim] + # Final embedding, merge multiple heads into one. -> [bs * n_agents, n_head, rnn_hidden_dim] + embedding = embedding_own + self.unify_input_heads( + embedding_enemies + embedding_allies + ) + + x = F.relu(embedding, inplace=True) + h_in = hidden_state.reshape(-1, self.rnn_hidden_dim) + hh = self.rnn(x, h_in) # [bs * n_agents, rnn_hidden_dim] + + # Q-values of normal actions + q_normal = self.output_normal_actions(hh).view(bs, self.n_agents, -1) # [bs, n_agents, 6] + + # Q-values of attack actions: [bs * n_agents * n_enemies, enemy_fea_dim] -> [bs * n_agents * n_enemies, rnn_hidden_dim, 1 * n_heads] + output_w_attack = self.hyper_output_w_attack_action(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies, self.rnn_hidden_dim, self.n_heads + ).transpose(1, 2).reshape( # -> [bs * n_agents, rnn_hidden_dim, n_enemies, n_heads] + bs * self.n_agents, self.rnn_hidden_dim, self.n_enemies * self.n_heads + ) # [bs * n_agents, rnn_hidden_dim, n_enemies * heads] + # b: [bs * n_agents * n_enemies, enemy_fea_dim] -> [bs * n_agents * n_enemies, 1, n_heads] + output_b_attack = self.hyper_output_b_attack_action(enemy_feats_t).view( + bs * self.n_agents, self.n_enemies * self.n_heads + ) # -> [bs * n_agents, n_enemies * head] + + # [bs * n_agents, 1, rnn_hidden_dim] * [bs * n_agents, rnn_hidden_dim, n_enemies * head] = [bs * n_agents, 1, n_enemies * head] + # -> # [bs * n_agents, n_enemies * head] -> [bs * n_agents * n_enemies, head, 1] + q_attacks = (th.matmul(hh.unsqueeze(1), output_w_attack).squeeze(1) + output_b_attack).view(-1, self.n_heads, + 1) + q_attack = self.unify_output_heads(q_attacks).view( # [bs * n_agents * n_enemies, 1] + bs, self.n_agents, self.n_enemies + ) # [bs, n_agents, n_enemies] + + # %%%%%%%%%%%%%%% 'rescue' actions for map_type == "MMM" %%%%%%%%%%%%%%% + if self.args.map_type == "MMM": + output_w_rescue = self.hyper_output_w_rescue_action(ally_feats_t).view( + bs * self.n_agents, self.n_allies, self.rnn_hidden_dim, self.n_heads + ).transpose(1, 2).reshape( # -> [bs * n_agents, rnn_hidden_dim, n_allies, n_heads] + bs * self.n_agents, self.rnn_hidden_dim, self.n_allies * self.n_heads + ) # [bs * n_agents, rnn_hidden_dim, n_allies * heads] + # b: [bs * n_agents * n_allies, ally_fea_dim] -> [bs * n_agents * n_allies, 1, n_heads] + output_b_rescue = self.hyper_output_b_rescue_action(ally_feats_t).view( + bs * self.n_agents, self.n_allies * self.n_heads + ) # -> [bs * n_agents, n_allies * head] + + # [bs * n_agents, 1, rnn_hidden_dim] * [bs * n_agents, rnn_hidden_dim, n_allies * head] = [bs * n_agents, 1, n_allies * head] + # -> # [bs * n_agents, n_allies * head] -> [bs * n_agents * n_allies, head, 1] + q_rescue = (th.matmul(hh.unsqueeze(1), output_w_rescue).squeeze(1) + output_b_rescue).view( + -1, self.n_heads, 1) + q_rescue = self.unify_rescue_output_heads(q_rescue).view( # [bs * n_agents * n_allies, 1] + bs, self.n_agents, self.n_allies + ) # [bs, n_agents, n_allies] + # For the reason that medivac is the last indexed agent, so the rescue action idx -> [0, n_allies-1] + right_padding = th.ones_like(q_attack[:, -1:, self.n_allies:], requires_grad=False) * (-9999999) + modified_q_attack_of_medivac = th.cat([q_rescue[:, -1:, :], right_padding], dim=-1) + # Merge + q_attack = th.cat([q_attack[:, :-1], modified_q_attack_of_medivac], dim=1) + + # Concat 2 types of Q-values + q = th.cat((q_normal, q_attack), dim=-1) # [bs, n_agents, 6 + n_enemies] + return q.view(bs, self.n_agents, -1), hh.view(bs, self.n_agents, -1) diff --git a/src/modules/agents/hpns_rnn_agent.py b/src/modules/agents/hpns_rnn_agent.py new file mode 100644 index 0000000..0a6a4bc --- /dev/null +++ b/src/modules/agents/hpns_rnn_agent.py @@ -0,0 +1,218 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import math +from torch.nn.parameter import Parameter + + +def kaiming_uniform_(tensor_w, tensor_b, mode='fan_in', gain=12 ** (-0.5)): + fan = nn.init._calculate_correct_fan(tensor_w.data, mode) + std = gain / math.sqrt(fan) + bound_w = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + bound_b = 1 / math.sqrt(fan) + with th.no_grad(): + tensor_w.data.uniform_(-bound_w, bound_w) + if tensor_b is not None: + tensor_b.data.uniform_(-bound_b, bound_b) + + +class Merger(nn.Module): + def __init__(self, head, fea_dim): + super(Merger, self).__init__() + self.head = head + if head > 1: + self.weight = Parameter(th.Tensor(1, head, fea_dim).fill_(1.)) + self.softmax = nn.Softmax(dim=1) + + def forward(self, x): + """ + :param x: [bs, n_head, fea_dim] + :return: [bs, fea_dim] + """ + if self.head > 1: + return th.sum(self.softmax(self.weight) * x, dim=1, keepdim=False) + else: + return th.squeeze(x, dim=1) + + +class HPNS_RNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(HPNS_RNNAgent, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_allies = args.n_allies + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + self.n_heads = args.hpn_head_num + self.rnn_hidden_dim = args.rnn_hidden_dim + + # [4 + 1, (6, 5), (4, 5)] + self.own_feats_dim, self.enemy_feats_dim, self.ally_feats_dim = input_shape + self.enemy_feats_dim = self.enemy_feats_dim[-1] # [n_enemies, feat_dim] + self.ally_feats_dim = self.ally_feats_dim[-1] # [n_allies, feat_dim] + + if self.args.obs_agent_id: + # embedding table for agent_id + self.agent_id_embedding = th.nn.Embedding(self.n_agents, self.rnn_hidden_dim) + + if self.args.obs_last_action: + # embedding table for action id + self.action_id_embedding = th.nn.Embedding(self.n_actions, self.rnn_hidden_dim) + + # Unique Features (do not need hyper net) + self.fc1_own = nn.Linear(self.own_feats_dim, self.rnn_hidden_dim, bias=True) # only one bias is OK + + # Multiple entities (use hyper net to process these features to ensure permutation invariant) + self.hyper_enemy = nn.Sequential( + nn.Linear(self.enemy_feats_dim, args.hpn_hyper_dim), + nn.ReLU(inplace=True), + nn.Linear(args.hpn_hyper_dim, ((self.enemy_feats_dim + 1) * self.rnn_hidden_dim + 1) * self.n_heads) + ) # output shape: (enemy_feats_dim * rnn_hidden_dim + rnn_hidden_dim + 1) + + if self.args.map_type == "MMM": + assert self.n_enemies >= self.n_agents, "For MMM map, for the reason that the 'attack' and 'rescue' use the same ids in SMAC, n_enemies must >= n_agents" + self.hyper_ally = nn.Sequential( + nn.Linear(self.ally_feats_dim, args.hpn_hyper_dim), + nn.ReLU(inplace=True), + nn.Linear(args.hpn_hyper_dim, ((self.ally_feats_dim + 1) * self.rnn_hidden_dim + 1) * self.n_heads) + ) # output shape: ally_feats_dim * rnn_hidden_dim + rnn_hidden_dim + 1, for 'rescue actions' + self.unify_output_heads_rescue = Merger(self.n_heads, 1) + else: + self.hyper_ally = nn.Sequential( + nn.Linear(self.ally_feats_dim, args.hpn_hyper_dim), + nn.ReLU(inplace=True), + nn.Linear(args.hpn_hyper_dim, self.ally_feats_dim * self.rnn_hidden_dim * self.n_heads) + ) # output shape: ally_feats_dim * rnn_hidden_dim + + self.unify_input_heads = Merger(self.n_heads, self.rnn_hidden_dim) + self.rnn = nn.GRUCell(self.rnn_hidden_dim, self.rnn_hidden_dim) + self.fc2_normal_actions = nn.Linear(self.rnn_hidden_dim, args.output_normal_actions) # (no_op, stop, up, down, right, left) + self.unify_output_heads = Merger(self.n_heads, 1) + + # Reset parameters for hypernets + # self._reset_hypernet_parameters(init_type="xavier") + # self._reset_hypernet_parameters(init_type="kaiming") + + def _reset_hypernet_parameters(self, init_type='kaiming'): + gain = 2 ** (-0.5) + # %%%%%%%%%%%%%%%%%%%%%% Hypernet-based API input layer %%%%%%%%%%%%%%%%%%%% + for m in self.hyper_enemy.modules(): + if isinstance(m, nn.Linear): + if init_type == "kaiming": + kaiming_uniform_(m.weight, m.bias, gain=gain) + else: + nn.init.xavier_normal_(m.weight.data) + m.bias.data.fill_(0.) + for m in self.hyper_ally.modules(): + if isinstance(m, nn.Linear): + if init_type == "kaiming": + kaiming_uniform_(m.weight, m.bias, gain=gain) + else: + nn.init.xavier_normal_(m.weight.data) + m.bias.data.fill_(0.) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1_own.weight.new(1, self.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state): + # [bs, n_agents, mv_fea_dim], [bs * n_agents * n_enemies, enemy_fea_dim], [bs * n_agents * n_allies, ally_fea_dim], [bs, n_agents, own_fea_dim] + bs, own_feats_t, enemy_feats_t, ally_feats_t, embedding_indices = inputs + + # (1) Own feature + embedding_own = self.fc1_own(own_feats_t) # [bs * n_agents, rnn_hidden_dim] + + # (2) ID embeddings + if self.args.obs_agent_id: + agent_indices = embedding_indices[0] + # [bs * n_agents, rnn_hidden_dim] + embedding_own = embedding_own + self.agent_id_embedding(agent_indices).view(-1, self.rnn_hidden_dim) + if self.args.obs_last_action: + last_action_indices = embedding_indices[-1] + if last_action_indices is not None: # t != 0 + # [bs * n_agents, rnn_hidden_dim] + embedding_own = embedding_own + self.action_id_embedding(last_action_indices).view( + -1, self.rnn_hidden_dim) + + # (3) Enemy feature (enemy_feats_dim * rnn_hidden_dim + rnn_hidden_dim + 1) + hyper_enemy_out = self.hyper_enemy(enemy_feats_t) + fc1_w_enemy = hyper_enemy_out[:, :-(self.rnn_hidden_dim + 1) * self.n_heads].reshape( + -1, self.enemy_feats_dim, self.rnn_hidden_dim * self.n_heads + ) # [bs * n_agents * n_enemies, enemy_fea_dim, rnn_hidden_dim] + # [bs * n_agents * n_enemies, 1, enemy_fea_dim] * [bs * n_agents * n_enemies, enemy_fea_dim, rnn_hidden_dim] = [bs * n_agents * n_enemies, 1, rnn_hidden_dim] + embedding_enemies = th.matmul(enemy_feats_t.unsqueeze(1), fc1_w_enemy).view( + bs * self.n_agents, self.n_enemies, self.n_heads, self.rnn_hidden_dim + ) # [bs * n_agents, n_enemies, n_heads, rnn_hidden_dim] + embedding_enemies = embedding_enemies.sum(dim=1, keepdim=False) # [bs * n_agents, n_heads, rnn_hidden_dim] + + # (4) Ally features + hyper_ally_out = self.hyper_ally(ally_feats_t) + if self.args.map_type == "MMM": + # [bs * n_agents * n_allies, ally_fea_dim, rnn_hidden_dim * head] + fc1_w_ally = hyper_ally_out[:, :-(self.rnn_hidden_dim + 1) * self.n_heads].reshape( + -1, self.ally_feats_dim, self.rnn_hidden_dim * self.n_heads + ) + else: + # [bs * n_agents * n_allies, ally_fea_dim, rnn_hidden_dim * head] + fc1_w_ally = hyper_ally_out.view(-1, self.ally_feats_dim, self.rnn_hidden_dim * self.n_heads) + # [bs * n_agents * n_allies, 1, ally_fea_dim] * [bs * n_agents * n_allies, ally_fea_dim, n_heads* rnn_hidden_dim] = [bs * n_agents * n_allies, 1, n_heads*rnn_hidden_dim] + embedding_allies = th.matmul(ally_feats_t.unsqueeze(1), fc1_w_ally).view( + bs * self.n_agents, self.n_allies, self.n_heads, self.rnn_hidden_dim + ) # [bs * n_agents, n_allies, head, rnn_hidden_dim] + embedding_allies = embedding_allies.sum(dim=1, keepdim=False) # [bs * n_agents, head, rnn_hidden_dim] + + # Final embedding + embedding = embedding_own + self.unify_input_heads( + embedding_enemies + embedding_allies + ) # [bs * n_agents, head, rnn_hidden_dim] + + x = F.relu(embedding, inplace=True) + h_in = hidden_state.reshape(-1, self.rnn_hidden_dim) + hh = self.rnn(x, h_in) # [bs * n_agents, rnn_hidden_dim] + + # Q-values of normal actions + q_normal = self.fc2_normal_actions(hh).view(bs, self.n_agents, -1) # [bs, n_agents, 6] + + # Q-values of attack actions: [bs * n_agents * n_enemies, rnn_hidden_dim * n_heads] + fc2_w_attack = hyper_enemy_out[:, -(self.rnn_hidden_dim + 1) * self.n_heads: -self.n_heads].reshape( + bs * self.n_agents, self.n_enemies, self.rnn_hidden_dim, self.n_heads + ).transpose(1, 2).reshape( # -> [bs * n_agents, rnn_hidden_dim, n_enemies, n_heads] + bs * self.n_agents, self.rnn_hidden_dim, self.n_enemies * self.n_heads + ) # [bs * n_agents, rnn_hidden_dim, n_enemies * heads] + fc2_b_attack = hyper_enemy_out[:, -self.n_heads:].reshape(bs * self.n_agents, self.n_enemies * self.n_heads) + + # [bs*n_agents, 1, rnn_hidden_dim] * [bs*n_agents, rnn_hidden_dim, n_enemies*head] -> [bs*n_agents, 1, n_enemies*head] + q_attacks = (th.matmul(hh.unsqueeze(1), fc2_w_attack).squeeze(1) + fc2_b_attack).view( + bs * self.n_agents * self.n_enemies, self.n_heads, 1 + ) # [bs * n_agents, n_enemies*head] -> [bs * n_agents * n_enemies, head, 1] + + # Merge multiple heads into one. + q_attack = self.unify_output_heads(q_attacks).view( # [bs * n_agents * n_enemies, 1] + bs, self.n_agents, self.n_enemies + ) # [bs, n_agents, n_enemies] + + # %%%%%%%%%%%%%%% 'rescue' actions for map_type == "MMM" %%%%%%%%%%%%%%% + if self.args.map_type == "MMM": + fc2_w_rescue = hyper_ally_out[:, -(self.rnn_hidden_dim + 1) * self.n_heads: -self.n_heads].reshape( + bs * self.n_agents, self.n_allies, self.rnn_hidden_dim, self.n_heads + ).transpose(1, 2).reshape( # -> [bs * n_agents, rnn_hidden_dim, n_allies, n_heads] + bs * self.n_agents, self.rnn_hidden_dim, self.n_allies * self.n_heads + ) # [bs * n_agents, rnn_hidden_dim, n_allies * heads] + fc2_b_rescue = hyper_ally_out[:, -self.n_heads:].reshape(bs * self.n_agents, self.n_allies * self.n_heads) + # [bs*n_agents, 1, rnn_hidden_dim] * [bs*n_agents, rnn_hidden_dim, n_allies*head] -> [bs*n_agents, 1, n_allies*head] + q_rescues = (th.matmul(hh.unsqueeze(1), fc2_w_rescue).squeeze(1) + fc2_b_rescue).view( + bs * self.n_agents * self.n_allies, self.n_heads, 1 + ) # [bs * n_agents, n_allies*head] -> [bs * n_agents * n_allies, head, 1] + # Merge multiple heads into one. + q_rescue = self.unify_output_heads_rescue(q_rescues).view( # [bs * n_agents * n_allies, 1] + bs, self.n_agents, self.n_allies + ) # [bs, n_agents, n_allies] + + # For the reason that medivac is the last indexed agent, so the rescue action idx -> [0, n_allies-1] + right_padding = th.ones_like(q_attack[:, -1:, self.n_allies:], requires_grad=False) * (-9999999) + modified_q_attack_of_medivac = th.cat([q_rescue[:, -1:, :], right_padding], dim=-1) + q_attack = th.cat([q_attack[:, :-1], modified_q_attack_of_medivac], dim=1) + + # Concat 2 types of Q-values + q = th.cat((q_normal, q_attack), dim=-1) # [bs, n_agents, 6 + n_enemies] + return q.view(bs, self.n_agents, -1), hh.view(bs, self.n_agents, -1) # [bs, n_agents, 6 + n_enemies] diff --git a/src/modules/agents/n_rnn_agent.py b/src/modules/agents/n_rnn_agent.py new file mode 100644 index 0000000..ae67686 --- /dev/null +++ b/src/modules/agents/n_rnn_agent.py @@ -0,0 +1,46 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch as th +import numpy as np +import torch.nn.init as init +from utils.th_utils import orthogonal_init_ +from torch.nn import LayerNorm + + +class NRNNAgent(nn.Module): + """ + n_rnn 30.412K for 5m_vs_6m + """ + def __init__(self, input_shape, args): + super(NRNNAgent, self).__init__() + self.args = args + + self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim) + self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim) + self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions) + + if getattr(args, "use_layer_norm", False): + self.layer_norm = LayerNorm(args.rnn_hidden_dim) + + if getattr(args, "use_orthogonal", False): + orthogonal_init_(self.fc1) + orthogonal_init_(self.fc2, gain=args.gain) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1.weight.new(1, self.args.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state): + b, a, e = inputs.size() + + inputs = inputs.view(-1, e) + x = F.relu(self.fc1(inputs), inplace=True) + h_in = hidden_state.reshape(-1, self.args.rnn_hidden_dim) + hh = self.rnn(x, h_in) + + if getattr(self.args, "use_layer_norm", False): + q = self.fc2(self.layer_norm(hh)) + else: + q = self.fc2(hh) + + return q.view(b, a, -1), hh.view(b, a, -1) \ No newline at end of file diff --git a/src/modules/agents/rnn_agent.py b/src/modules/agents/rnn_agent.py new file mode 100644 index 0000000..1c0c1ab --- /dev/null +++ b/src/modules/agents/rnn_agent.py @@ -0,0 +1,26 @@ +import torch.nn as nn +import torch.nn.functional as F + + +class RNNAgent(nn.Module): + def __init__(self, input_shape, args): + super(RNNAgent, self).__init__() + self.args = args + + self.fc1 = nn.Linear(input_shape, args.rnn_hidden_dim) + self.rnn = nn.GRUCell(args.rnn_hidden_dim, args.rnn_hidden_dim) + self.fc2 = nn.Linear(args.rnn_hidden_dim, args.n_actions) + + def init_hidden(self): + # make hidden states on same device as model + return self.fc1.weight.new(1, self.args.rnn_hidden_dim).zero_() + + def forward(self, inputs, hidden_state=None): + b, a, e = inputs.size() + + x = F.relu(self.fc1(inputs.view(-1, e)), inplace=True) + if hidden_state is not None: + hidden_state = hidden_state.reshape(-1, self.args.rnn_hidden_dim) + h = self.rnn(x, hidden_state) + q = self.fc2(h) + return q.view(b, a, -1), h.view(b, a, -1) \ No newline at end of file diff --git a/src/modules/agents/updet_agent.py b/src/modules/agents/updet_agent.py new file mode 100644 index 0000000..959cde0 --- /dev/null +++ b/src/modules/agents/updet_agent.py @@ -0,0 +1,205 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch +import argparse +import torch as th + + +class UPDeT(nn.Module): + def __init__(self, input_shape, args): + super(UPDeT, self).__init__() + self.args = args + self.input_shape = input_shape # (5, (6, 5), (4, 5)) for 5m vs 6m + self.n_agents = args.n_agents + + self.transformer = Transformer(input_shapes=input_shape, emb=args.transformer_embed_dim, + heads=args.transformer_heads, depth=args.transformer_depth, + output_dim=args.transformer_embed_dim) + self.q_basic = nn.Linear(args.transformer_embed_dim, 6) + + def init_hidden(self): + # make hidden states on same device as model + return self.q_basic.weight.new(1, self.args.transformer_embed_dim).zero_() + + def forward(self, inputs, hidden_state): + # (bs * n_agents, 1, transformer_embed_dim] + hidden_state = hidden_state.reshape(-1, 1, self.args.transformer_embed_dim) + + # transformer-out: torch.Size([b * n_agents, 1+n_enemies+(n_agents-1)+1, transformer_embed_dim]) + # in dim 1: self_fea_att_value, m enemy_fea_att_value, n-1 ally_fea_att_value, hidden_state + outputs, _ = self.transformer.forward( + inputs, hidden_state, None) + + # first output for 6 action (no_op stop up down left right) + q_basic_actions = self.q_basic(outputs[:, 0, :]) + + # last dim for hidden state + h = outputs[:, -1:, :] + + # Replace the loop with batch computing + # q_enemies_list = [] + # # each enemy has an output Q + # for i in range(self.args.n_enemies): + # q_enemy = self.q_basic(outputs[:, 1 + i, :]) + # q_enemy_mean = torch.mean(q_enemy, 1, True) + # q_enemies_list.append(q_enemy_mean) + # # concat enemy Q over all enemies + # q_enemies = torch.stack(q_enemies_list, dim=1).squeeze() + + q_enemies = self.q_basic( + outputs[:, 1: 1 + self.args.n_enemies, :]) # [bs * n_agents, n_enemies, 32]->[bs * n_agents, n_enemies, 6] + q_enemies = q_enemies.mean(dim=-1, keepdim=False) # The average of the Move Action Q + + # concat basic action Q with enemy attack Q + q = torch.cat((q_basic_actions, q_enemies), 1) + + return q, h # [bs * n_agents, 6 + n_enemies], this shape will be reshaped to [bs, n_agents, 6 + n_enemies] in forward() of the BasicMAC + + +class SelfAttention(nn.Module): + def __init__(self, emb, heads=8, mask=False): + super(SelfAttention, self).__init__() + + self.emb = emb + self.heads = heads + self.mask = mask + + self.tokeys = nn.Linear(emb, emb * heads, bias=False) + self.toqueries = nn.Linear(emb, emb * heads, bias=False) + self.tovalues = nn.Linear(emb, emb * heads, bias=False) + + self.unifyheads = nn.Linear(heads * emb, emb) + + def forward(self, x, mask): + + b, t, e = x.size() + h = self.heads + keys = self.tokeys(x).view(b, t, h, e) + queries = self.toqueries(x).view(b, t, h, e) + values = self.tovalues(x).view(b, t, h, e) + + # compute scaled dot-product self-attention + + # - fold heads into the batch dimension + keys = keys.transpose(1, 2).contiguous().view(b * h, t, e) + queries = queries.transpose(1, 2).contiguous().view(b * h, t, e) + values = values.transpose(1, 2).contiguous().view(b * h, t, e) + + queries = queries / (e ** (1 / 4)) + keys = keys / (e ** (1 / 4)) + # - Instead of dividing the dot products by sqrt(e), we scale the keys and values. + # This should be more memory efficient + + # - get dot product of queries and keys, and scale + dot = torch.bmm(queries, keys.transpose(1, 2)) + + assert dot.size() == (b * h, t, t) + + if self.mask: # mask out the upper half of the dot matrix, excluding the diagonal + mask_(dot, maskval=float('-inf'), mask_diagonal=False) + + if mask is not None: + dot = dot.masked_fill(mask == 0, -1e9) + + dot = F.softmax(dot, dim=2) + # - dot now has row-wise self-attention probabilities + + # apply the self attention to the values + out = torch.bmm(dot, values).view(b, h, t, e) + + # swap h, t back, unify heads + out = out.transpose(1, 2).contiguous().view(b, t, h * e) + + return self.unifyheads(out) # [b, n_entities, e] + + +class TransformerBlock(nn.Module): + + def __init__(self, emb, heads, mask, ff_hidden_mult=4, dropout=0.0): + super(TransformerBlock, self).__init__() + self.attention = SelfAttention(emb, heads=heads, mask=mask) + self.mask = mask + self.norm1 = nn.LayerNorm(emb) + self.norm2 = nn.LayerNorm(emb) + self.ff = nn.Sequential( + nn.Linear(emb, ff_hidden_mult * emb), + nn.ReLU(), + nn.Linear(ff_hidden_mult * emb, emb) + ) + self.do = nn.Dropout(dropout) + + def forward(self, x_mask): + x, mask = x_mask + attended = self.attention(x, mask) + x = self.norm1(attended + x) + x = self.do(x) + fedforward = self.ff(x) + x = self.norm2(fedforward + x) + x = self.do(x) + return x, mask + + +class Transformer(nn.Module): + + def __init__(self, input_shapes, emb, heads, depth, output_dim): + super(Transformer, self).__init__() + self.num_tokens = output_dim + + self.input_shapes = input_shapes # (5, (6, 5), (4, 5)) for 5m vs 6m + # use the max_dim to init the token layer (to support all maps) + token_dim = max([input_shapes[0], input_shapes[1][-1], input_shapes[2][-1]]) + self.token_embedding = nn.Linear(token_dim, emb) + + tblocks = [] + for i in range(depth): + tblocks.append(TransformerBlock(emb=emb, heads=heads, mask=False)) + self.tblocks = nn.Sequential(*tblocks) + self.toprobs = nn.Linear(emb, output_dim) + + def forward(self, inputs, h, mask): + """ + + :param inputs: cat([(bs * n_agents, 1, -1), (bs * n_agents, n_enemies, -1), (bs * n_agents, (n_agents-1), -1)], dim=1) + :param h: + :param mask: + :return: + """ + tokens = self.token_embedding(inputs) # (bs * n_agents, 1 + n_enemies + (n_agents-1), emb) + + # Append hidden state to the end + tokens = torch.cat((tokens, h), 1) # tokens+h: torch.Size([5, 12, 32]) + + b, t, e = tokens.size() + + x, mask = self.tblocks((tokens, mask)) + # print("transformer-out:", x.shape) # transformer-out: torch.Size([5, 12, 32]) + + x = self.toprobs(x.view(b * t, e)).view(b, t, self.num_tokens) # torch.Size([5, 12, 32]) + return x, tokens + + +def mask_(matrices, maskval=0.0, mask_diagonal=True): + b, h, w = matrices.size() + indices = torch.triu_indices(h, w, offset=0 if mask_diagonal else 1) + matrices[:, indices[0], indices[1]] = maskval + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Unit Testing') + parser.add_argument('--token_dim', default='5', type=int) + parser.add_argument('--emb', default='32', type=int) + parser.add_argument('--heads', default='3', type=int) + parser.add_argument('--depth', default='2', type=int) + parser.add_argument('--ally_num', default='5', type=int) + parser.add_argument('--enemy_num', default='5', type=int) + parser.add_argument('--episode', default='20', type=int) + args = parser.parse_args() + + # testing the agent + agent = UPDeT(None, args).cuda() + hidden_state = agent.init_hidden().cuda().expand(args.ally_num, 1, -1) + tensor = torch.rand(args.ally_num, args.ally_num + args.enemy_num, args.token_dim).cuda() + q_list = [] + for _ in range(args.episode): + q, hidden_state = agent.forward(tensor, hidden_state, args.ally_num, args.enemy_num) + q_list.append(q) diff --git a/src/modules/critics/__init__.py b/src/modules/critics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/modules/critics/centralv.py b/src/modules/critics/centralv.py new file mode 100644 index 0000000..71e580e --- /dev/null +++ b/src/modules/critics/centralv.py @@ -0,0 +1,36 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class CentralVCritic(nn.Module): + def __init__(self, scheme, args): + super(CentralVCritic, self).__init__() + + self.args = args + self.n_actions = args.n_actions + self.n_agents = args.n_agents + + input_shape = self._get_input_shape(scheme) + self.output_type = "v" + + # Set up network layers + self.fc1 = nn.Sequential(nn.Linear(input_shape, 256), + nn.ReLU(inplace=True), + nn.Linear(256, 256), + nn.ReLU(inplace=True), + nn.Linear(256, 1) + ) + + def forward(self, batch, t=None): + inputs = self._build_inputs(batch, t=t) + q = self.fc1(inputs) + return q + + def _build_inputs(self, batch, t=None): + ts = slice(None) if t is None else slice(t, t+1) + return batch["state"][:, ts] + + def _get_input_shape(self, scheme): + input_shape = scheme["state"]["vshape"] + return input_shape \ No newline at end of file diff --git a/src/modules/critics/coma.py b/src/modules/critics/coma.py new file mode 100644 index 0000000..ddc9ab2 --- /dev/null +++ b/src/modules/critics/coma.py @@ -0,0 +1,70 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class COMACritic(nn.Module): + def __init__(self, scheme, args): + super(COMACritic, self).__init__() + + self.args = args + self.n_actions = args.n_actions + self.n_agents = args.n_agents + + input_shape = self._get_input_shape(scheme) + self.output_type = "q" + + # Set up network layers + self.fc1 = nn.Linear(input_shape, 128) + self.fc2 = nn.Linear(128, 128) + self.fc3 = nn.Linear(128, self.n_actions) + + def forward(self, batch, t=None): + inputs = self._build_inputs(batch, t=t) + x = F.relu(self.fc1(inputs)) + x = F.relu(self.fc2(x)) + q = self.fc3(x) + return q + + def _build_inputs(self, batch, t=None): + bs = batch.batch_size + max_t = batch.max_seq_length if t is None else 1 + ts = slice(None) if t is None else slice(t, t+1) + inputs = [] + # state + inputs.append(batch["state"][:, ts].unsqueeze(2).repeat(1, 1, self.n_agents, 1)) + + # observation + inputs.append(batch["obs"][:, ts]) + + # actions (masked out by agent) + actions = batch["actions_onehot"][:, ts].view(bs, max_t, 1, -1).repeat(1, 1, self.n_agents, 1) + agent_mask = (1 - th.eye(self.n_agents, device=batch.device)) + agent_mask = agent_mask.view(-1, 1).repeat(1, self.n_actions).view(self.n_agents, -1) + inputs.append(actions * agent_mask.unsqueeze(0).unsqueeze(0)) + + # last actions + if t == 0: + inputs.append(th.zeros_like(batch["actions_onehot"][:, 0:1]).view(bs, max_t, 1, -1).repeat(1, 1, self.n_agents, 1)) + elif isinstance(t, int): + inputs.append(batch["actions_onehot"][:, slice(t-1, t)].view(bs, max_t, 1, -1).repeat(1, 1, self.n_agents, 1)) + else: + last_actions = th.cat([th.zeros_like(batch["actions_onehot"][:, 0:1]), batch["actions_onehot"][:, :-1]], dim=1) + last_actions = last_actions.view(bs, max_t, 1, -1).repeat(1, 1, self.n_agents, 1) + inputs.append(last_actions) + + inputs.append(th.eye(self.n_agents, device=batch.device).unsqueeze(0).unsqueeze(0).expand(bs, max_t, -1, -1)) + + inputs = th.cat([x.reshape(bs, max_t, self.n_agents, -1) for x in inputs], dim=-1) + return inputs + + def _get_input_shape(self, scheme): + # state + input_shape = scheme["state"]["vshape"] + # observation + input_shape += scheme["obs"]["vshape"] + # actions and last actions + input_shape += scheme["actions_onehot"]["vshape"][0] * self.n_agents * 2 + # agent id + input_shape += self.n_agents + return input_shape \ No newline at end of file diff --git a/src/modules/critics/fmac_critic.py b/src/modules/critics/fmac_critic.py new file mode 100644 index 0000000..df70c07 --- /dev/null +++ b/src/modules/critics/fmac_critic.py @@ -0,0 +1,56 @@ +from numpy.core.numeric import True_ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from modules.layer.self_atten import SelfAttention + + +class FMACCritic(nn.Module): + def __init__(self, scheme, args): + super(FMACCritic, self).__init__() + self.args = args + self.n_actions = args.n_actions + self.n_agents = args.n_agents + self.input_shape = self._get_input_shape(scheme) + self.output_type = "q" + self.hidden_states = None + self.critic_hidden_dim = args.critic_hidden_dim + + # Set up network layers + self.fc1 = nn.Linear(self.input_shape + self.n_actions, self.critic_hidden_dim) + self.fc2 = nn.Linear(self.critic_hidden_dim, self.critic_hidden_dim) + self.fc3 = nn.Linear(self.critic_hidden_dim, 1) + + def forward(self, inputs, actions, hidden_state=None): + bs = inputs.batch_size + ts = inputs.max_seq_length + + inputs = self._build_inputs(inputs) + inputs = th.cat([inputs, actions], dim=-1) + x = F.relu(self.fc1(inputs), inplace=True) + x = F.relu(self.fc2(x), inplace=True) + q1 = self.fc3(x) + + return q1, hidden_state + + def _build_inputs(self, batch): + # Assumes homogenous agents with flat observations. + # Other MACs might want to e.g. delegate building inputs to each agent + bs = batch.batch_size + ts = batch.max_seq_length + inputs = [] + inputs.append(batch["obs"]) # b1av + # inputs.append(batch["state"].unsqueeze(2).repeat(1, 1, self.n_agents, 1)) # b1av + if self.args.obs_agent_id: + inputs.append(th.eye(self.n_agents, device=batch.device)\ + .unsqueeze(0).unsqueeze(0).expand(bs, ts, -1, -1)) + inputs = th.cat([x.reshape(bs, ts, self.n_agents, -1) for x in inputs], dim=-1) + return inputs + + def _get_input_shape(self, scheme): + input_shape = scheme["obs"]["vshape"] + # input_shape += scheme["state"]["vshape"] + if self.args.obs_agent_id: + input_shape += self.n_agents + + return input_shape \ No newline at end of file diff --git a/src/modules/critics/lica.py b/src/modules/critics/lica.py new file mode 100644 index 0000000..d814c1e --- /dev/null +++ b/src/modules/critics/lica.py @@ -0,0 +1,57 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class LICACritic(nn.Module): + def __init__(self, scheme, args): + super(LICACritic, self).__init__() + + self.args = args + self.n_actions = args.n_actions + self.n_agents = args.n_agents + + self.output_type = "q" + + # Set up network layers + self.state_dim = int(np.prod(args.state_shape)) + self.weight_dim = args.lica_mixing_embed_dim * self.n_agents * self.n_actions + self.hid_dim = args.hypernet_embed_dim + + self.hyper_w_1 = nn.Sequential(nn.Linear(self.state_dim, self.hid_dim), + nn.ReLU(), + nn.Linear(self.hid_dim , self.weight_dim)) + self.hyper_w_final = nn.Sequential(nn.Linear(self.state_dim, self.hid_dim), + nn.ReLU(), + nn.Linear(self.hid_dim, args.lica_mixing_embed_dim)) + + # State dependent bias for hidden layer + self.hyper_b_1 = nn.Linear(self.state_dim, args.lica_mixing_embed_dim) + + self.hyper_b_2 = nn.Sequential(nn.Linear(self.state_dim, self.hid_dim), + nn.ReLU(), + nn.Linear(self.hid_dim, 1)) + + def forward(self, act, states): + bs = states.size(0) + states = states.reshape(-1, self.state_dim) + action_probs = act.reshape(-1, 1, self.n_agents * self.n_actions) + + # first layer + w1 = self.hyper_w_1(states) + b1 = self.hyper_b_1(states) + w1 = w1.view(-1, self.n_agents * self.n_actions, self.args.lica_mixing_embed_dim) + b1 = b1.view(-1, 1, self.args.lica_mixing_embed_dim) + + h = th.relu(th.bmm(action_probs, w1) + b1) + + # second layer + w_final = self.hyper_w_final(states) + w_final = w_final.view(-1, self.args.lica_mixing_embed_dim, 1) + b2 = self.hyper_b_2(states).view(-1, 1, 1) + + q = th.bmm(h, w_final )+ b2 + q = q.view(bs, -1, 1) + + return q \ No newline at end of file diff --git a/src/modules/critics/offpg.py b/src/modules/critics/offpg.py new file mode 100644 index 0000000..73bbf81 --- /dev/null +++ b/src/modules/critics/offpg.py @@ -0,0 +1,65 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F + + +class OffPGCritic(nn.Module): + def __init__(self, scheme, args): + super(OffPGCritic, self).__init__() + + self.args = args + self.n_actions = args.n_actions + self.n_agents = args.n_agents + + input_shape = self._get_input_shape(scheme) + self.output_type = "q" + + # Set up network layers + self.fc1 = nn.Linear(input_shape, 256) + self.fc2 = nn.Linear(256, 256) + self.fc_v = nn.Linear(256, 1) + self.fc3 = nn.Linear(256, self.n_actions) + + def forward(self, inputs): + x = F.relu(self.fc1(inputs)) + x = F.relu(self.fc2(x)) + v = self.fc_v(x) + a = self.fc3(x) + q = a + v + return q + + def _build_inputs(self, batch, bs, max_t): + inputs = [] + # state, obs, action + inputs.append(batch["state"][:].unsqueeze(2).repeat(1, 1, self.n_agents, 1)) + inputs.append(batch["obs"][:]) + #actions = batch["actions_onehot"][:].view(bs, max_t, 1, -1).repeat(1, 1, self.n_agents, 1) + #agent_mask = (1 - th.eye(self.n_agents, device=batch.device)) + #agent_mask = agent_mask.view(-1, 1).repeat(1, self.n_actions).view(self.n_agents, -1) + #inputs.append(actions * agent_mask.unsqueeze(0).unsqueeze(0)) + # last actions + #if self.args.obs_last_action: + # last_action = [] + # last_action.append(actions[:, 0:1].squeeze(2)) + # last_action.append(actions[:, :-1].squeeze(2)) + # last_action = th.cat([x for x in last_action], dim = 1) + # inputs.append(last_action) + #agent id + inputs.append(th.eye(self.n_agents, device=batch.device).unsqueeze(0).unsqueeze(0).expand(bs, max_t, -1, -1)) + inputs = th.cat([x.reshape(bs, max_t, self.n_agents, -1) for x in inputs], dim=-1) + return inputs + + + + def _get_input_shape(self, scheme): + # state + input_shape = scheme["state"]["vshape"] + # observation + input_shape += scheme["obs"]["vshape"] + # actions and last actions + #input_shape += scheme["actions_onehot"]["vshape"][0] * self.n_agents + #if self.args.obs_last_action: + # input_shape += scheme["actions_onehot"]["vshape"][0] * self.n_agents + # agent id + input_shape += self.n_agents + return input_shape diff --git a/src/modules/layer/__init__.py b/src/modules/layer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/modules/layer/self_atten.py b/src/modules/layer/self_atten.py new file mode 100644 index 0000000..5d58f87 --- /dev/null +++ b/src/modules/layer/self_atten.py @@ -0,0 +1,43 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class SelfAttention(nn.Module): + def __init__(self, input_size, heads, embed_size): + super().__init__() + self.input_size = input_size + self.heads = heads + self.emb_size = embed_size + + self.tokeys = nn.Linear(self.input_size, self.emb_size * heads, bias = False) + self.toqueries = nn.Linear(self.input_size, self.emb_size * heads, bias = False) + self.tovalues = nn.Linear(self.input_size, self.emb_size * heads, bias = False) + + def forward(self, x): + b, t, hin = x.size() + assert hin == self.input_size, 'Input size {} should match {}'.format(hin, self.input_size) + + h = self.heads + e = self.emb_size + + keys = self.tokeys(x).view(b, t, h, e) + queries = self.toqueries(x).view(b, t, h, e) + values = self.tovalues(x).view(b, t, h, e) + + # dot-product attention + # folding heads to batch dimensions + keys = keys.transpose(1, 2).contiguous().view(b * h, t, e) + queries = queries.transpose(1, 2).contiguous().view(b * h, t, e) + values = values.transpose(1, 2).contiguous().view(b * h, t, e) + + queries = queries / (e ** (1/4)) + keys = keys / (e ** (1/4)) + + dot = torch.bmm(queries, keys.transpose(1, 2)) + assert dot.size() == (b*h, t, t) + + # row wise self attention probabilities + dot = F.softmax(dot, dim=2) + out = torch.bmm(dot, values).view(b, h, t, e) + out = out.transpose(1, 2).contiguous().view(b, t, h * e) + return out \ No newline at end of file diff --git a/src/modules/mixers/__init__.py b/src/modules/mixers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/modules/mixers/api_qmix.py b/src/modules/mixers/api_qmix.py new file mode 100644 index 0000000..927485f --- /dev/null +++ b/src/modules/mixers/api_qmix.py @@ -0,0 +1,74 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from utils.th_utils import orthogonal_init_ +from torch.nn import LayerNorm + +class Mixer(nn.Module): + def __init__(self, args, abs=True): + super(Mixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.embed_dim = args.mixing_embed_dim + self.input_dim = self.state_dim = int(np.prod(args.state_shape)) + + self.abs = abs # monotonicity constraint + self.qmix_pos_func = getattr(self.args, "qmix_pos_func", "abs") + assert self.qmix_pos_func == "abs" + + # hyper w1 b1 + self.hyper_w1 = nn.Sequential(nn.Linear(self.input_dim, args.hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(args.hypernet_embed, self.n_agents * self.embed_dim)) + self.hyper_b1 = nn.Sequential(nn.Linear(self.input_dim, self.embed_dim)) + + # hyper w2 b2 + self.hyper_w2 = nn.Sequential(nn.Linear(self.input_dim, args.hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(args.hypernet_embed, self.embed_dim)) + self.hyper_b2 = nn.Sequential(nn.Linear(self.input_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + + if getattr(args, "use_orthogonal", False): + raise NotImplementedError + for m in self.modules(): + orthogonal_init_(m) + + def forward(self, qvals, states): + # reshape + b, t, _ = qvals.size() + + qvals = qvals.reshape(b * t, 1, self.n_agents) + states = states.reshape(-1, self.state_dim) + + # First layer + w1 = self.hyper_w1(states).view(-1, self.n_agents, self.embed_dim) # b * t, n_agents, emb + b1 = self.hyper_b1(states).view(-1, 1, self.embed_dim) + + # Second layer + w2 = self.hyper_w2(states).view(-1, self.embed_dim, 1) # b * t, emb, 1 + b2= self.hyper_b2(states).view(-1, 1, 1) + + if self.abs: + w1 = self.pos_func(w1) + w2 = self.pos_func(w2) + # print(w1.mean(), w1.var()) + # print(w2.mean(), w2.var()) + + # Forward + hidden = F.elu(th.matmul(qvals, w1) + b1) # b * t, 1, emb + y = th.matmul(hidden, w2) + b2 # b * t, 1, 1 + + return y.view(b, t, -1) + + def pos_func(self, x): + if self.qmix_pos_func == "softplus": + return th.nn.Softplus(beta=self.args.qmix_pos_func_beta)(x) + elif self.qmix_pos_func == "quadratic": + return 0.5 * x ** 2 + else: + return th.abs(x) + diff --git a/src/modules/mixers/dmaq_general.py b/src/modules/mixers/dmaq_general.py new file mode 100644 index 0000000..0aa1357 --- /dev/null +++ b/src/modules/mixers/dmaq_general.py @@ -0,0 +1,92 @@ +# From https://github.com/wjh720/QPLEX/, added here for convenience. +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from .dmaq_si_weight import DMAQ_SI_Weight + + +class DMAQer(nn.Module): + def __init__(self, args): + super(DMAQer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.n_actions = args.n_actions + self.state_dim = int(np.prod(args.state_shape)) + self.action_dim = args.n_agents * self.n_actions + self.state_action_dim = self.state_dim + self.action_dim + 1 + + self.embed_dim = args.mixing_embed_dim + + hypernet_embed = self.args.hypernet_embed + self.hyper_w_final = nn.Sequential(nn.Linear(self.state_dim, hypernet_embed), + nn.ReLU(), + nn.Linear(hypernet_embed, self.n_agents)) + self.V = nn.Sequential(nn.Linear(self.state_dim, hypernet_embed), + nn.ReLU(), + nn.Linear(hypernet_embed, self.n_agents)) + + self.si_weight = DMAQ_SI_Weight(args) + + def calc_v(self, agent_qs): + agent_qs = agent_qs.view(-1, self.n_agents) + v_tot = th.sum(agent_qs, dim=-1) + return v_tot + + def calc_adv(self, agent_qs, states, actions, max_q_i): + states = states.reshape(-1, self.state_dim) + actions = actions.reshape(-1, self.action_dim) + agent_qs = agent_qs.view(-1, self.n_agents) + max_q_i = max_q_i.view(-1, self.n_agents) + + adv_q = (agent_qs - max_q_i).view(-1, self.n_agents).detach() + + adv_w_final = self.si_weight(states, actions) + adv_w_final = adv_w_final.view(-1, self.n_agents) + + if self.args.is_minus_one: + adv_tot = th.sum(adv_q * (adv_w_final - 1.), dim=1) + else: + adv_tot = th.sum(adv_q * adv_w_final, dim=1) + return adv_tot + + def calc(self, agent_qs, states, actions=None, max_q_i=None, is_v=False): + if is_v: + v_tot = self.calc_v(agent_qs) + return v_tot + else: + adv_tot = self.calc_adv(agent_qs, states, actions, max_q_i) + return adv_tot + + def forward(self, agent_qs, states, actions=None, max_q_i=None, is_v=False): + """ + :param agent_qs: selected individual Q(s,a) + :param states: + :param actions: + :param max_q_i: + :param is_v: + :return: + """ + bs = agent_qs.size(0) + states = states.reshape(-1, self.state_dim) + agent_qs = agent_qs.view(-1, self.n_agents) + + w_final = self.hyper_w_final(states) + w_final = th.abs(w_final) + w_final = w_final.view(-1, self.n_agents) + 1e-10 + v = self.V(states) + v = v.view(-1, self.n_agents) + + if self.args.weighted_head: + agent_qs = w_final * agent_qs + v + + if not is_v: + max_q_i = max_q_i.view(-1, self.n_agents) + if self.args.weighted_head: + max_q_i = w_final * max_q_i + v + + y = self.calc(agent_qs, states, actions=actions, max_q_i=max_q_i, is_v=is_v) + v_tot = y.view(bs, -1, 1) + + return v_tot diff --git a/src/modules/mixers/dmaq_si_weight.py b/src/modules/mixers/dmaq_si_weight.py new file mode 100644 index 0000000..da11a38 --- /dev/null +++ b/src/modules/mixers/dmaq_si_weight.py @@ -0,0 +1,81 @@ +# From https://github.com/wjh720/QPLEX/, added here for convenience. +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class DMAQ_SI_Weight(nn.Module): + def __init__(self, args): + super(DMAQ_SI_Weight, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.n_actions = args.n_actions + self.state_dim = int(np.prod(args.state_shape)) + self.action_dim = args.n_agents * self.n_actions + self.state_action_dim = self.state_dim + self.action_dim + + self.num_kernel = args.num_kernel + + self.key_extractors = nn.ModuleList() + self.agents_extractors = nn.ModuleList() + self.action_extractors = nn.ModuleList() + + adv_hypernet_embed = self.args.adv_hypernet_embed + for i in range(self.num_kernel): # multi-head attention + if getattr(args, "adv_hypernet_layers", 1) == 1: + self.key_extractors.append(nn.Linear(self.state_dim, 1)) # key + self.agents_extractors.append(nn.Linear(self.state_dim, self.n_agents)) # agent + self.action_extractors.append(nn.Linear(self.state_action_dim, self.n_agents)) # action + elif getattr(args, "adv_hypernet_layers", 1) == 2: + self.key_extractors.append(nn.Sequential(nn.Linear(self.state_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, 1))) # key + self.agents_extractors.append(nn.Sequential(nn.Linear(self.state_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, self.n_agents))) # agent + self.action_extractors.append(nn.Sequential(nn.Linear(self.state_action_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, self.n_agents))) # action + elif getattr(args, "adv_hypernet_layers", 1) == 3: + self.key_extractors.append(nn.Sequential(nn.Linear(self.state_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, 1))) # key + self.agents_extractors.append(nn.Sequential(nn.Linear(self.state_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, self.n_agents))) # agent + self.action_extractors.append(nn.Sequential(nn.Linear(self.state_action_dim, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, adv_hypernet_embed), + nn.ReLU(), + nn.Linear(adv_hypernet_embed, self.n_agents))) # action + else: + raise Exception("Error setting number of adv hypernet layers.") + + def forward(self, states, actions): + states = states.reshape(-1, self.state_dim) + actions = actions.reshape(-1, self.action_dim) + data = th.cat([states, actions], dim=1) + + all_head_key = [k_ext(states) for k_ext in self.key_extractors] + all_head_agents = [k_ext(states) for k_ext in self.agents_extractors] + all_head_action = [sel_ext(data) for sel_ext in self.action_extractors] + + head_attend_weights = [] + for curr_head_key, curr_head_agents, curr_head_action in zip(all_head_key, all_head_agents, all_head_action): + x_key = th.abs(curr_head_key).repeat(1, self.n_agents) + 1e-10 + x_agents = F.sigmoid(curr_head_agents) + x_action = F.sigmoid(curr_head_action) + weights = x_key * x_agents * x_action + head_attend_weights.append(weights) + + head_attend = th.stack(head_attend_weights, dim=1) + head_attend = head_attend.view(-1, self.num_kernel, self.n_agents) + head_attend = th.sum(head_attend, dim=1) + + return head_attend diff --git a/src/modules/mixers/nmix.py b/src/modules/mixers/nmix.py new file mode 100644 index 0000000..927485f --- /dev/null +++ b/src/modules/mixers/nmix.py @@ -0,0 +1,74 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from utils.th_utils import orthogonal_init_ +from torch.nn import LayerNorm + +class Mixer(nn.Module): + def __init__(self, args, abs=True): + super(Mixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.embed_dim = args.mixing_embed_dim + self.input_dim = self.state_dim = int(np.prod(args.state_shape)) + + self.abs = abs # monotonicity constraint + self.qmix_pos_func = getattr(self.args, "qmix_pos_func", "abs") + assert self.qmix_pos_func == "abs" + + # hyper w1 b1 + self.hyper_w1 = nn.Sequential(nn.Linear(self.input_dim, args.hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(args.hypernet_embed, self.n_agents * self.embed_dim)) + self.hyper_b1 = nn.Sequential(nn.Linear(self.input_dim, self.embed_dim)) + + # hyper w2 b2 + self.hyper_w2 = nn.Sequential(nn.Linear(self.input_dim, args.hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(args.hypernet_embed, self.embed_dim)) + self.hyper_b2 = nn.Sequential(nn.Linear(self.input_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + + if getattr(args, "use_orthogonal", False): + raise NotImplementedError + for m in self.modules(): + orthogonal_init_(m) + + def forward(self, qvals, states): + # reshape + b, t, _ = qvals.size() + + qvals = qvals.reshape(b * t, 1, self.n_agents) + states = states.reshape(-1, self.state_dim) + + # First layer + w1 = self.hyper_w1(states).view(-1, self.n_agents, self.embed_dim) # b * t, n_agents, emb + b1 = self.hyper_b1(states).view(-1, 1, self.embed_dim) + + # Second layer + w2 = self.hyper_w2(states).view(-1, self.embed_dim, 1) # b * t, emb, 1 + b2= self.hyper_b2(states).view(-1, 1, 1) + + if self.abs: + w1 = self.pos_func(w1) + w2 = self.pos_func(w2) + # print(w1.mean(), w1.var()) + # print(w2.mean(), w2.var()) + + # Forward + hidden = F.elu(th.matmul(qvals, w1) + b1) # b * t, 1, emb + y = th.matmul(hidden, w2) + b2 # b * t, 1, 1 + + return y.view(b, t, -1) + + def pos_func(self, x): + if self.qmix_pos_func == "softplus": + return th.nn.Softplus(beta=self.args.qmix_pos_func_beta)(x) + elif self.qmix_pos_func == "quadratic": + return 0.5 * x ** 2 + else: + return th.abs(x) + diff --git a/src/modules/mixers/qatten.py b/src/modules/mixers/qatten.py new file mode 100644 index 0000000..aab641a --- /dev/null +++ b/src/modules/mixers/qatten.py @@ -0,0 +1,100 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class QattenMixer(nn.Module): + def __init__(self, args): + super(QattenMixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.state_dim = int(np.prod(args.state_shape)) + self.u_dim = int(np.prod(args.agent_own_state_size)) + + self.n_query_embedding_layer1 = args.n_query_embedding_layer1 + self.n_query_embedding_layer2 = args.n_query_embedding_layer2 + self.n_key_embedding_layer1 = args.n_key_embedding_layer1 + self.n_head_embedding_layer1 = args.n_head_embedding_layer1 + self.n_head_embedding_layer2 = args.n_head_embedding_layer2 + self.n_attention_head = args.n_attention_head + self.n_constrant_value = args.n_constrant_value + + self.query_embedding_layers = nn.ModuleList() + for i in range(self.n_attention_head): + self.query_embedding_layers.append(nn.Sequential(nn.Linear(self.state_dim, self.n_query_embedding_layer1), + nn.ReLU(), + nn.Linear(self.n_query_embedding_layer1, self.n_query_embedding_layer2))) + + self.key_embedding_layers = nn.ModuleList() + for i in range(self.n_attention_head): + self.key_embedding_layers.append(nn.Linear(self.u_dim, self.n_key_embedding_layer1)) + + + self.scaled_product_value = np.sqrt(args.n_query_embedding_layer2) + + self.head_embedding_layer = nn.Sequential(nn.Linear(self.state_dim, self.n_head_embedding_layer1), + nn.ReLU(), + nn.Linear(self.n_head_embedding_layer1, self.n_head_embedding_layer2)) + + self.constrant_value_layer = nn.Sequential(nn.Linear(self.state_dim, self.n_constrant_value), + nn.ReLU(), + nn.Linear(self.n_constrant_value, 1)) + + + def forward(self, agent_qs, states): + bs = agent_qs.size(0) + states = states.reshape(-1, self.state_dim) + us = self._get_us(states) + agent_qs = agent_qs.view(-1, 1, self.n_agents) + + q_lambda_list = [] + for i in range(self.n_attention_head): + state_embedding = self.query_embedding_layers[i](states) + u_embedding = self.key_embedding_layers[i](us) + + # shape: [-1, 1, state_dim] + state_embedding = state_embedding.reshape(-1, 1, self.n_query_embedding_layer2) + # shape: [-1, state_dim, n_agent] + u_embedding = u_embedding.reshape(-1, self.n_agents, self.n_key_embedding_layer1) + u_embedding = u_embedding.permute(0, 2, 1) + + # shape: [-1, 1, n_agent] + raw_lambda = th.matmul(state_embedding, u_embedding) / self.scaled_product_value + q_lambda = F.softmax(raw_lambda, dim=-1) + + q_lambda_list.append(q_lambda) + + # shape: [-1, n_attention_head, n_agent] + q_lambda_list = th.stack(q_lambda_list, dim=1).squeeze(-2) + + # shape: [-1, n_agent, n_attention_head] + q_lambda_list = q_lambda_list.permute(0, 2, 1) + + # shape: [-1, 1, n_attention_head] + q_h = th.matmul(agent_qs, q_lambda_list) + + if self.args.type == 'weighted': + # shape: [-1, n_attention_head, 1] + w_h = th.abs(self.head_embedding_layer(states)) + w_h = w_h.reshape(-1, self.n_head_embedding_layer2, 1) + + # shape: [-1, 1] + sum_q_h = th.matmul(q_h, w_h) + sum_q_h = sum_q_h.reshape(-1, 1) + else: + # shape: [-1, 1] + sum_q_h = q_h.sum(-1) + sum_q_h = sum_q_h.reshape(-1, 1) + + c = self.constrant_value_layer(states) + q_tot = sum_q_h + c + q_tot = q_tot.view(bs, -1, 1) + return q_tot + + def _get_us(self, states): + agent_own_state_size = self.args.agent_own_state_size + with th.no_grad(): + us = states[:, :agent_own_state_size*self.n_agents].reshape(-1, agent_own_state_size) + return us \ No newline at end of file diff --git a/src/modules/mixers/qmix.py b/src/modules/mixers/qmix.py new file mode 100644 index 0000000..f67516a --- /dev/null +++ b/src/modules/mixers/qmix.py @@ -0,0 +1,84 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class QMixer(nn.Module): + def __init__(self, args): + super(QMixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.state_dim = int(np.prod(args.state_shape)) + + self.embed_dim = args.mixing_embed_dim + self.abs = getattr(self.args, 'abs', True) + + if getattr(args, "hypernet_layers", 1) == 1: + self.hyper_w_1 = nn.Linear(self.state_dim, self.embed_dim * self.n_agents) + self.hyper_w_final = nn.Linear(self.state_dim, self.embed_dim) + elif getattr(args, "hypernet_layers", 1) == 2: + hypernet_embed = self.args.hypernet_embed + self.hyper_w_1 = nn.Sequential(nn.Linear(self.state_dim, hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(hypernet_embed, self.embed_dim * self.n_agents)) + self.hyper_w_final = nn.Sequential(nn.Linear(self.state_dim, hypernet_embed), + nn.ReLU(inplace=True), + nn.Linear(hypernet_embed, self.embed_dim)) + elif getattr(args, "hypernet_layers", 1) > 2: + raise Exception("Sorry >2 hypernet layers is not implemented!") + else: + raise Exception("Error setting number of hypernet layers.") + + # State dependent bias for hidden layer + self.hyper_b_1 = nn.Linear(self.state_dim, self.embed_dim) + + # V(s) instead of a bias for the last layers + self.V = nn.Sequential(nn.Linear(self.state_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + + + def forward(self, agent_qs, states): + bs = agent_qs.size(0) + states = states.reshape(-1, self.state_dim) + agent_qs = agent_qs.reshape(-1, 1, self.n_agents) + # First layer + w1 = self.hyper_w_1(states).abs() if self.abs else self.hyper_w_1(states) + b1 = self.hyper_b_1(states) + w1 = w1.view(-1, self.n_agents, self.embed_dim) + b1 = b1.view(-1, 1, self.embed_dim) + hidden = F.elu(th.bmm(agent_qs, w1) + b1) + + # Second layer + w_final = self.hyper_w_final(states).abs() if self.abs else self.hyper_w_final(states) + w_final = w_final.view(-1, self.embed_dim, 1) + # State-dependent bias + v = self.V(states).view(-1, 1, 1) + # Compute final output + y = th.bmm(hidden, w_final) + v + # Reshape and return + q_tot = y.view(bs, -1, 1) + + return q_tot + + def k(self, states): + bs = states.size(0) + w1 = th.abs(self.hyper_w_1(states)) + w_final = th.abs(self.hyper_w_final(states)) + w1 = w1.view(-1, self.n_agents, self.embed_dim) + w_final = w_final.view(-1, self.embed_dim, 1) + k = th.bmm(w1,w_final).view(bs, -1, self.n_agents) + k = k / th.sum(k, dim=2, keepdim=True) + return k + + def b(self, states): + bs = states.size(0) + w_final = th.abs(self.hyper_w_final(states)) + w_final = w_final.view(-1, self.embed_dim, 1) + b1 = self.hyper_b_1(states) + b1 = b1.view(-1, 1, self.embed_dim) + v = self.V(states).view(-1, 1, 1) + b = th.bmm(b1, w_final) + v + return b diff --git a/src/modules/mixers/qtran.py b/src/modules/mixers/qtran.py new file mode 100644 index 0000000..96e8f82 --- /dev/null +++ b/src/modules/mixers/qtran.py @@ -0,0 +1,105 @@ +import torch as th +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +class QTranBase(nn.Module): + def __init__(self, args): + super(QTranBase, self).__init__() + + self.args = args + + self.n_agents = args.n_agents + self.n_actions = args.n_actions + self.state_dim = int(np.prod(args.state_shape)) + self.arch = self.args.qtran_arch # QTran architecture + + self.embed_dim = args.mixing_embed_dim + + # Q(s,u) + if self.arch == "coma_critic": + # Q takes [state, u] as input + q_input_size = self.state_dim + (self.n_agents * self.n_actions) + elif self.arch == "qtran_paper": + # Q takes [state, agent_action_observation_encodings] + q_input_size = self.state_dim + self.args.rnn_hidden_dim + self.n_actions + else: + raise Exception("{} is not a valid QTran architecture".format(self.arch)) + + if self.args.network_size == "small": + self.Q = nn.Sequential(nn.Linear(q_input_size, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + + # V(s) + self.V = nn.Sequential(nn.Linear(self.state_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + ae_input = self.args.rnn_hidden_dim + self.n_actions + self.action_encoding = nn.Sequential(nn.Linear(ae_input, ae_input), + nn.ReLU(inplace=True), + nn.Linear(ae_input, ae_input)) + elif self.args.network_size == "big": + self.Q = nn.Sequential(nn.Linear(q_input_size, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + # V(s) + self.V = nn.Sequential(nn.Linear(self.state_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, self.embed_dim), + nn.ReLU(inplace=True), + nn.Linear(self.embed_dim, 1)) + ae_input = self.args.rnn_hidden_dim + self.n_actions + self.action_encoding = nn.Sequential(nn.Linear(ae_input, ae_input), + nn.ReLU(inplace=True), + nn.Linear(ae_input, ae_input)) + else: + assert False + + def forward(self, batch, hidden_states, actions=None): + bs = batch.batch_size + ts = batch.max_seq_length + + states = batch["state"].reshape(bs * ts, self.state_dim) + + if self.arch == "coma_critic": + if actions is None: + # Use the actions taken by the agents + actions = batch["actions_onehot"].reshape(bs * ts, self.n_agents * self.n_actions) + else: + # It will arrive as (bs, ts, agents, actions), we need to reshape it + actions = actions.reshape(bs * ts, self.n_agents * self.n_actions) + inputs = th.cat([states, actions], dim=1) + elif self.arch == "qtran_paper": + if actions is None: + # Use the actions taken by the agents + actions = batch["actions_onehot"].reshape(bs * ts, self.n_agents, self.n_actions) + else: + # It will arrive as (bs, ts, agents, actions), we need to reshape it + actions = actions.reshape(bs * ts, self.n_agents, self.n_actions) + + hidden_states = hidden_states.reshape(bs * ts, self.n_agents, -1) + agent_state_action_input = th.cat([hidden_states, actions], dim=2) + agent_state_action_encoding = self.action_encoding(agent_state_action_input.reshape(bs * ts * self.n_agents, -1)).reshape(bs * ts, self.n_agents, -1) + agent_state_action_encoding = agent_state_action_encoding.sum(dim=1) # Sum across agents + + inputs = th.cat([states, agent_state_action_encoding], dim=1) + + q_outputs = self.Q(inputs) + + states = batch["state"].reshape(bs * ts, self.state_dim) + v_outputs = self.V(states) + + return q_outputs, v_outputs + diff --git a/src/modules/mixers/qtransformer.py b/src/modules/mixers/qtransformer.py new file mode 100644 index 0000000..791d816 --- /dev/null +++ b/src/modules/mixers/qtransformer.py @@ -0,0 +1,158 @@ +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parameter import Parameter + + +class HyperLinear(nn.Module): + def __init__(self, entity_num, input_dim, output_dim, hyper_hidden_size, bias=True): + super(HyperLinear, self).__init__() + self.normalize = False + self.entity_num = entity_num + self.input_dim = input_dim + self.output_dim = output_dim + + self.hypernet = nn.Sequential( + nn.Linear(input_dim, hyper_hidden_size), + nn.ReLU(inplace=True), + nn.Linear(hyper_hidden_size, input_dim * output_dim), + # nn.Tanh() + ) + if bias: + self.bias = Parameter(th.Tensor(1, output_dim).fill_(0.)) + else: + self.bias = 0 + + def forward(self, x): + bs, fea_dim = x.shape + hyper_out = self.hypernet(x) + + if self.normalize: + # [batch_size, input_dim * output_dim] -> [b * t, entity_num, input_dim, output_dim] + hyper_out = F.softmax(hyper_out.view(-1, self.entity_num, self.input_dim, self.output_dim), dim=1) + + # [batch_size, input_dim * output_dim] -> [batch_size, input_dim, output_dim] + weights = hyper_out.view(bs, self.input_dim, self.output_dim) + + out = th.matmul(x.unsqueeze(1), weights).squeeze(1) + self.bias + return out # [batch_size output_dim] + + +class APIEmbeddingLayer(nn.Module): + def __init__(self, args, output_dim): + super(APIEmbeddingLayer, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + self.output_dim = output_dim + + self.embedding_enemy = HyperLinear(args.n_enemies, args.state_enemy_feats_size, output_dim, args.hpn_hyper_dim) + self.embedding_ally = HyperLinear(args.n_agents, args.state_ally_feats_size, output_dim, args.hpn_hyper_dim) + + if self.args.env_args["state_last_action"]: + self.embedding_action = nn.Linear(args.n_actions, output_dim) + + if self.args.env_args["state_timestep_number"]: + self.embedding_timestep = nn.Linear(1, output_dim) + + def forward(self, state_components): + ally_features, enemy_features = state_components[:2] + ally_features = ally_features.reshape(-1, self.args.state_ally_feats_size) + enemy_features = enemy_features.reshape(-1, self.args.state_enemy_feats_size) + + # [bs * t, output_dim] + embed_ally = self.embedding_ally(ally_features).view(-1, self.n_agents, self.output_dim).mean(dim=1) + embed_enemy = self.embedding_enemy(enemy_features).view(-1, self.n_enemies, self.output_dim).mean(dim=1) + output = embed_ally + embed_enemy + + if self.args.env_args["state_last_action"]: + n_agent_actions = state_components[2].reshape(-1, self.n_agents, self.n_actions) + embed_last_action = self.embedding_action(n_agent_actions).mean(dim=1) # [bs * t,output_dim] + output = output + embed_last_action + + if self.args.env_args["state_timestep_number"]: + timestep = state_components[-1] + embed_timestep = self.embedding_timestep(timestep) # [bs * t, output_dim] + output = output + embed_timestep + + return output + + +class APIMixer(nn.Module): + """ + The Mixing Net should be permutation invariant. + """ + + def __init__(self, args): + super(APIMixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.embed_dim = args.mixing_embed_dim + self.input_dim = self.state_dim = int(np.prod(args.state_shape)) + + # hyper w1 + self.hyper_w1 = nn.Sequential( + nn.Linear(args.rnn_hidden_dim, 1 * self.embed_dim) + ) + + # shared PI state embedding + self.shared_state_embedding = nn.Sequential( + APIEmbeddingLayer(args, args.hypernet_embed), + nn.ReLU(inplace=True), + ) + + # hyper b1 + self.hyper_b1 = nn.Sequential( + nn.Linear(args.hypernet_embed, self.embed_dim) + ) + + # hyper w2 b2 + self.hyper_w2 = nn.Sequential( + nn.Linear(args.hypernet_embed, self.embed_dim) + ) + self.hyper_b2 = nn.Sequential( + nn.Linear(args.hypernet_embed, 1) + ) + + def forward(self, qvals, states, hidden_states): + """ + :param qvals: individual Q + :param states: global state + :param hidden_states: GRU output of the agent network, [bs, traj_len, n_agents, hidden_dim] + :return: + """ + # reshape + b, t, _ = qvals.size() + + qvals = qvals.reshape(b * t, 1, self.n_agents) + states = states.reshape(-1, self.state_dim) + state_components = th.split(states, self.args.state_component, dim=-1) + + # Shared state embedding + state_embedding = self.shared_state_embedding(state_components) # [bs * t, hypernet_embed] + + # First layer + w1 = self.hyper_w1(hidden_states).view(-1, self.n_agents, self.embed_dim) # [b * t, n_agents, emb] + w1 = F.softmax(w1, dim=1) # already be positive + # [b * t, 1, n_agents] * [b * t, n_agents, emb] + + b1 = self.hyper_b1(state_embedding).view(-1, 1, self.embed_dim) + + # Second layer + w2 = self.hyper_w2(state_embedding).view(-1, self.embed_dim, 1) # [b * t, emb, 1] + b2 = self.hyper_b2(state_embedding).view(-1, 1, 1) # [b * t, 1, 1] + + # positive weight + # w1 = th.abs(w1) + w2 = th.abs(w2) + # print(w1.mean(), w1.var()) + # print(w2.mean(), w2.var()) + + # Forward + hidden = F.elu(th.matmul(qvals, w1) + b1) # [b * t, 1, emb] + y = th.matmul(hidden, w2) + b2 # b * t, 1, 1 + + return y.view(b, t, -1) diff --git a/src/modules/mixers/qtransformer_v0.py b/src/modules/mixers/qtransformer_v0.py new file mode 100644 index 0000000..5e1495f --- /dev/null +++ b/src/modules/mixers/qtransformer_v0.py @@ -0,0 +1,182 @@ +import numpy as np +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parameter import Parameter + + +class TokenLayer(nn.Module): + def __init__(self, args, token_dim): + super(TokenLayer, self).__init__() + self.args = args + self.n_agents = args.n_agents + self.n_enemies = args.n_enemies + self.n_actions = args.n_actions + + self.embedding_ally = nn.Linear(args.state_ally_feats_size, token_dim) + self.embedding_enemy = nn.Linear(args.state_enemy_feats_size, token_dim) + + if self.args.env_args["state_last_action"]: + self.embedding_action = nn.Linear(args.n_actions, token_dim) + + if self.args.env_args["state_timestep_number"]: + self.embedding_timestep = nn.Linear(1, token_dim) + + def forward(self, state_components): + ally_features, enemy_features = state_components[:2] + ally_features = ally_features.reshape(-1, self.n_agents, self.args.state_ally_feats_size) + enemy_features = enemy_features.reshape(-1, self.n_enemies, self.args.state_enemy_feats_size) + + embed_ally = self.embedding_ally(ally_features) # [bs * t, n_agents, embed_dim] + embed_enemy = self.embedding_enemy(enemy_features) # [bs * t, n_enemies, embed_dim] + tokens = [embed_ally, embed_enemy] + + if self.args.env_args["state_last_action"]: + n_agent_actions = state_components[2].reshape(-1, self.n_agents, self.n_actions) + embed_last_action = self.embedding_action(n_agent_actions) # [bs * t, n_agents, embed_dim] + tokens.append(embed_last_action) + + if self.args.env_args["state_timestep_number"]: + timestep = state_components[-1] + embed_timestep = self.embedding_timestep(timestep).unsqueeze(dim=-2) # [bs * t, 1, embed_dim] + tokens.append(embed_timestep) + + tokens = th.cat(tokens, dim=-2) + return tokens # [bs * t, entity_num, embed_dim] + + +class SelfAttention(nn.Module): + def __init__(self, emb_dim, shared_query, end_index, heads=1): + super(SelfAttention, self).__init__() + + self.emb_dim = emb_dim + self.heads = heads + self.shared_query = shared_query + self.end_index = end_index + + if shared_query: + self.queries = Parameter(th.Tensor(1, emb_dim * heads)) + nn.init.normal_(self.queries) + self.end_index = 1 + else: + self.toqueries = nn.Linear(emb_dim, emb_dim * heads, bias=False) + self.tokeys = nn.Linear(emb_dim, emb_dim * heads, bias=False) + self.tovalues = nn.Linear(emb_dim, emb_dim * heads, bias=False) + + if self.heads > 1: + self.unifyheads = nn.Linear(heads * emb_dim, emb_dim) + + def forward(self, x): + b, t, e = x.size() # [bs, sequence_length, token_dim] + h = self.heads + + if self.shared_query: + queries = self.queries.expand(b, -1).view(b, 1, h, e) + else: + queries = self.toqueries(x).view(b, t, h, e) + keys = self.tokeys(x).view(b, t, h, e) + values = self.tovalues(x).view(b, t, h, e) + + # compute scaled dot-product self-attention + + # - fold heads into the batch dimension + if self.shared_query: + queries = queries.transpose(1, 2).contiguous().view(b * h, 1, e) + else: + queries = queries.transpose(1, 2).contiguous().view(b * h, t, e) + keys = keys.transpose(1, 2).contiguous().view(b * h, t, e) + values = values.transpose(1, 2).contiguous().view(b * h, t, e) + + # - Instead of dividing the dot products by sqrt(e), we scale the keys and values. + # This should be more memory efficient + queries = queries[:, :self.end_index] / (e ** (1 / 4)) # [b * h, entity_num, e] + keys = keys / (e ** (1 / 4)) # [b * h, t, e] + + # - get dot product of queries and keys, and scale + dot = th.bmm(queries, keys.transpose(1, 2)) # [b * h, entity_num, t] + + assert dot.size() == (b * h, self.end_index, t) + + # - dot now has row-wise self-attention probabilities + dot = F.softmax(dot, dim=2) # [b * h, entity_num, t] + + # apply the self attention to the values, [b * h, entity_num, t] * [b * h, t, token_dim] = [b * h, entity_num, token_dim] + out = th.bmm(dot, values).view(b, h, self.end_index, e) # [b, h, entity_num, token_dim] + + # swap h, t back, unify heads + out = out.transpose(1, 2).contiguous().view(b, self.end_index, h * e) + + if self.heads > 1: + return self.unifyheads(out) # [b, entity_num, token_dim] + else: + return out # [b, entity_num, token_dim] + + +class TransformerMixer(nn.Module): + """ + The Mixing Net should be permutation invariant. + """ + + def __init__(self, args): + super(TransformerMixer, self).__init__() + + self.args = args + self.n_agents = args.n_agents + self.embed_dim = args.mixing_embed_dim + self.input_dim = self.state_dim = int(np.prod(args.state_shape)) + + # hyper w1 b1 + self.hyper_w1 = nn.Sequential( + TokenLayer(args, args.hypernet_embed), + SelfAttention(args.hypernet_embed, shared_query=False, end_index=self.n_agents, heads=1), + nn.Linear(args.hypernet_embed, self.embed_dim) + ) + self.hyper_b1 = nn.Sequential( + TokenLayer(args, self.embed_dim), + SelfAttention(self.embed_dim, shared_query=True, end_index=1, heads=1) + ) + + # hyper w2 b2 + self.hyper_w2 = nn.Sequential( + TokenLayer(args, args.hypernet_embed), + SelfAttention(args.hypernet_embed, shared_query=True, end_index=1, heads=1), + nn.Linear(args.hypernet_embed, self.embed_dim) + ) + self.hyper_b2 = nn.Sequential( + TokenLayer(args, self.embed_dim), + SelfAttention(self.embed_dim, shared_query=True, end_index=1, heads=1), + nn.Linear(self.embed_dim, 1) + ) + + def forward(self, qvals, states): + """ + :param qvals: individual Q + :param states: global state + :return: + """ + # reshape + b, t, _ = qvals.size() + + qvals = qvals.view(b * t, 1, self.n_agents) + states = states.reshape(-1, self.state_dim) + state_components = th.split(states, self.args.state_component, dim=-1) + + # First layer + w1 = self.hyper_w1(state_components).view(-1, self.n_agents, self.embed_dim) # [b * t, n_agents, emb] + b1 = self.hyper_b1(state_components).view(-1, 1, self.embed_dim) + + # Second layer + w2 = self.hyper_w2(state_components).view(-1, self.embed_dim, 1) # b * t, emb, 1 + b2 = self.hyper_b2(state_components).view(-1, 1, 1) + + # positive weight + w1 = th.abs(w1) + w2 = th.abs(w2) + # print(w1.mean(), w1.var()) + # print(w2.mean(), w2.var()) + + # Forward + hidden = F.elu(th.matmul(qvals, w1) + b1) # [b * t, 1, emb] + y = th.matmul(hidden, w2) + b2 # b * t, 1, 1 + + return y.view(b, t, -1) diff --git a/src/modules/mixers/vdn.py b/src/modules/mixers/vdn.py new file mode 100644 index 0000000..fc05b63 --- /dev/null +++ b/src/modules/mixers/vdn.py @@ -0,0 +1,10 @@ +import torch as th +import torch.nn as nn + + +class VDNMixer(nn.Module): + def __init__(self): + super(VDNMixer, self).__init__() + + def forward(self, agent_qs, batch): + return th.sum(agent_qs, dim=2, keepdim=True) \ No newline at end of file diff --git a/src/run/__init__.py b/src/run/__init__.py new file mode 100644 index 0000000..5383579 --- /dev/null +++ b/src/run/__init__.py @@ -0,0 +1,4 @@ +from .run import run as default_run + +REGISTRY = {} +REGISTRY["default"] = default_run \ No newline at end of file diff --git a/src/run/run.py b/src/run/run.py new file mode 100644 index 0000000..8775d48 --- /dev/null +++ b/src/run/run.py @@ -0,0 +1,334 @@ +import datetime +import os +import pprint +import time +import threading +import torch as th +from types import SimpleNamespace as SN +from utils.logging import Logger +from utils.timehelper import time_left, time_str +from os.path import dirname, abspath +#import sys + +from learners import REGISTRY as le_REGISTRY +from runners import REGISTRY as r_REGISTRY +from controllers import REGISTRY as mac_REGISTRY +from components.episode_buffer import ReplayBuffer +from components.transforms import OneHot + + +def run(_run, _config, _log): + # check args sanity + _config = args_sanity_check(_config, _log) + + args = SN(**_config) + + th.set_num_threads(args.thread_num) + # th.set_num_interop_threads(8) + + args.device = "cuda" if args.use_cuda else "cpu" + + # setup loggers + logger = Logger(_log) + + _log.info("Experiment Parameters:") + experiment_params = pprint.pformat(_config, + indent=4, + width=1) + _log.info("\n\n" + experiment_params + "\n") + + # configure tensorboard logger + unique_token = "{}__{}".format(args.name, datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) + args.unique_token = unique_token + + testing_algorithms = ["vdn", "qmix", "hpn_vdn", "hpn_qmix", + "deepset_vdn", "deepset_qmix", "deepset_hyper_vdn", "deepset_hyper_qmix", + "updet_vdn", "updet_qmix", "vdn_DA", "qmix_DA", + "gnn_vdn", "gnn_qmix", "qplex", "hpn_qplex", "asn" + ] + env_name = args.env + logdir = env_name + if env_name in ["sc2", "sc2_v2", ]: + logdir = os.path.join("{}_{}-obs_aid={}-obs_act={}".format( + logdir, + args.env_args["map_name"], + int(args.obs_agent_id), + int(args.obs_last_action), + )) + if env_name == "sc2_v2": + logdir = logdir + "-conic_fov={}".format( + "1-change_fov_by_move={}".format( + int(args.env_args["change_fov_with_move"])) if args.env_args["conic_fov"] else "0" + ) + logdir = os.path.join(logdir, + "algo={}-agent={}".format(args.name, args.agent), + "env_n={}".format( + args.batch_size_run, + )) + if args.name in testing_algorithms: + if args.name in ["vdn_DA", "qmix_DA", ]: + logdir = os.path.join(logdir, + "{}-data_augment={}".format( + args.mixer, args.augment_times + )) + elif args.name in ["gnn_vdn", "gnn_qmix"]: + logdir = os.path.join(logdir, + "{}-layer_num={}".format( + args.mixer, args.gnn_layer_num + )) + elif args.name in ["vdn", "qmix", "deepset_vdn", "deepset_qmix", "qplex", "asn"]: + logdir = os.path.join(logdir, + "mixer={}".format( + args.mixer, + )) + elif args.name in ["updet_vdn", "updet_qmix"]: + logdir = os.path.join(logdir, + "mixer={}-att_dim={}-att_head={}-att_layer={}".format( + args.mixer, + args.transformer_embed_dim, + args.transformer_heads, + args.transformer_depth, + )) + elif args.name in ["deepset_hyper_vdn", "deepset_hyper_qmix"]: + logdir = os.path.join(logdir, + "mixer={}-hpn_hyperdim={}".format( + args.mixer, + args.hpn_hyper_dim, + )) + elif args.name in ["hpn_vdn", "hpn_qmix", "hpn_qplex"]: + logdir = os.path.join(logdir, + "head_n={}-mixer={}-hpn_hyperdim={}-acti={}".format( + args.hpn_head_num, + args.mixer, + args.hpn_hyper_dim, + args.hpn_hyper_activation, + )) + + logdir = os.path.join(logdir, + "rnn_dim={}-2bs={}_{}-tdlambda={}-epdec_{}={}k".format( + args.rnn_hidden_dim, + args.buffer_size, + args.batch_size, + args.td_lambda, + args.epsilon_finish, + args.epsilon_anneal_time // 1000, + )) + args.log_model_dir = logdir + if args.use_tensorboard: + tb_logs_direc = os.path.join(dirname(dirname(dirname(abspath(__file__)))), args.local_results_path, "tb_logs") + tb_exp_direc = os.path.join(tb_logs_direc, "{}").format(unique_token) + if args.name in testing_algorithms: # add parameter config to the logger path！ + tb_exp_direc = os.path.join(tb_logs_direc, logdir, unique_token) + logger.setup_tb(tb_exp_direc) + + # sacred is on by default + logger.setup_sacred(_run) + + # Run and train + run_sequential(args=args, logger=logger) + + # Clean up after finishing + print("Exiting Main") + + print("Stopping all threads") + for t in threading.enumerate(): + if t.name != "MainThread": + print("Thread {} is alive! Is daemon: {}".format(t.name, t.daemon)) + t.join(timeout=1) + print("Thread joined") + + print("Exiting script") + + # Making sure framework really exits + os._exit(os.EX_OK) + + +def evaluate_sequential(args, runner): + for _ in range(args.test_nepisode): + runner.run(test_mode=True) + + if args.save_replay: + runner.save_replay() + + runner.close_env() + + +def run_sequential(args, logger): + # Init runner so we can get env info + runner = r_REGISTRY[args.runner](args=args, logger=logger) + + # Set up schemes and groups here + env_info = runner.get_env_info() + args.n_agents = env_info["n_agents"] + args.n_actions = env_info["n_actions"] + args.state_shape = env_info["state_shape"] + args.obs_shape = env_info["obs_shape"] + args.accumulated_episodes = getattr(args, "accumulated_episodes", None) + + if args.env in ["sc2", "sc2_v2", "gfootball"]: + if args.env in ["sc2", "sc2_v2"]: + args.output_normal_actions = env_info["n_normal_actions"] + args.n_enemies = env_info["n_enemies"] + args.n_allies = env_info["n_allies"] + # args.obs_ally_feats_size = env_info["obs_ally_feats_size"] + # args.obs_enemy_feats_size = env_info["obs_enemy_feats_size"] + args.state_ally_feats_size = env_info["state_ally_feats_size"] + args.state_enemy_feats_size = env_info["state_enemy_feats_size"] + args.obs_component = env_info["obs_component"] + args.state_component = env_info["state_component"] + args.map_type = env_info["map_type"] + args.agent_own_state_size = env_info["state_ally_feats_size"] + + # Default/Base scheme + scheme = { + "state": {"vshape": env_info["state_shape"]}, + "obs": {"vshape": env_info["obs_shape"], "group": "agents"}, + "actions": {"vshape": (1,), "group": "agents", "dtype": th.long}, + "avail_actions": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.int}, + "probs": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.float}, + "reward": {"vshape": (1,)}, + "terminated": {"vshape": (1,), "dtype": th.uint8}, + } + groups = { + "agents": args.n_agents + } + preprocess = { + "actions": ("actions_onehot", [OneHot(out_dim=args.n_actions)]) + } + # [batch, episode_length, n_agents, feature_dim] + buffer = ReplayBuffer(scheme, groups, args.buffer_size, env_info["episode_limit"] + 1, + preprocess=preprocess, + device="cpu" if args.buffer_cpu_only else args.device) + # Setup multiagent controller here + mac = mac_REGISTRY[args.mac](buffer.scheme, groups, args) + + # Give runner the scheme + runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac) + + # Learner + learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args) + + if args.use_cuda: + learner.cuda() + + if args.checkpoint_path != "": + timesteps = [] + timestep_to_load = 0 + + if not os.path.isdir(args.checkpoint_path): + logger.console_logger.info("Checkpoint directiory {} doesn't exist".format(args.checkpoint_path)) + return + + # Go through all files in args.checkpoint_path + for name in os.listdir(args.checkpoint_path): + full_name = os.path.join(args.checkpoint_path, name) + # Check if they are dirs the names of which are numbers + if os.path.isdir(full_name) and name.isdigit(): + timesteps.append(int(name)) + + if args.load_step == 0: + # choose the max timestep + timestep_to_load = max(timesteps) + else: + # choose the timestep closest to load_step + timestep_to_load = min(timesteps, key=lambda x: abs(x - args.load_step)) + + model_path = os.path.join(args.checkpoint_path, str(timestep_to_load)) + + logger.console_logger.info("Loading model from {}".format(model_path)) + learner.load_models(model_path) + runner.t_env = timestep_to_load + + if args.evaluate or args.save_replay: + evaluate_sequential(args, runner) + return + + # start training + episode = 0 + last_test_T = -args.test_interval - 1 + last_log_T = 0 + model_save_time = 0 + + start_time = time.time() + last_time = start_time + + logger.console_logger.info("Beginning training for {} timesteps".format(args.t_max)) + + while runner.t_env <= args.t_max: + # Run for a whole episode at a time + with th.no_grad(): + # t_start = time.time() + episode_batch = runner.run(test_mode=False) + if episode_batch.batch_size > 0: # After clearing the batch data, the batch may be empty. + buffer.insert_episode_batch(episode_batch) + # print("Sample new batch cost {} seconds.".format(time.time() - t_start)) + episode += args.batch_size_run + + if buffer.can_sample(args.batch_size): + if args.accumulated_episodes and episode % args.accumulated_episodes != 0: + continue + + episode_sample = buffer.sample(args.batch_size) + + # Truncate batch to only filled timesteps + max_ep_t = episode_sample.max_t_filled() + episode_sample = episode_sample[:, :max_ep_t] + + if episode_sample.device != args.device: + episode_sample.to(args.device) + + learner.train(episode_sample, runner.t_env, episode) + del episode_sample + + # Execute test runs once in a while + n_test_runs = max(1, args.test_nepisode // runner.batch_size) + if (runner.t_env - last_test_T) / args.test_interval >= 1.0: + logger.console_logger.info("t_env: {} / {}".format(runner.t_env, args.t_max)) + logger.console_logger.info("Estimated time left: {}. Time passed: {}".format( + time_left(last_time, last_test_T, runner.t_env, args.t_max), time_str(time.time() - start_time))) + last_time = time.time() + last_test_T = runner.t_env + with th.no_grad(): + for _ in range(n_test_runs): + runner.run(test_mode=True) + + if args.save_model and ( + runner.t_env - model_save_time >= args.save_model_interval or runner.t_env >= args.t_max): + model_save_time = runner.t_env + save_path = os.path.join(args.local_results_path, "models", args.log_model_dir, args.unique_token, + str(runner.t_env)) + # "results/models/{}".format(unique_token) + os.makedirs(save_path, exist_ok=True) + logger.console_logger.info("Saving models to {}".format(save_path)) + + # learner should handle saving/loading -- delegate actor save/load to mac, + # use appropriate filenames to do critics, optimizer states + learner.save_models(save_path) + + if (runner.t_env - last_log_T) >= args.log_interval: + logger.log_stat("episode", episode, runner.t_env) + logger.log_stat("episode_in_buffer", buffer.episodes_in_buffer, runner.t_env) + logger.print_recent_stats() + last_log_T = runner.t_env + + runner.close_env() + logger.console_logger.info("Finished Training") + + # flush + sys.stdout.flush() + time.sleep(10) + + +def args_sanity_check(config, _log): + # set CUDA flags + # config["use_cuda"] = True # Use cuda whenever possible! + if config["use_cuda"] and not th.cuda.is_available(): + config["use_cuda"] = False + _log.warning("CUDA flag use_cuda was switched OFF automatically because no CUDA devices are available!") + + if config["test_nepisode"] < config["batch_size_run"]: + config["test_nepisode"] = config["batch_size_run"] + else: + config["test_nepisode"] = (config["test_nepisode"] // config["batch_size_run"]) * config["batch_size_run"] + + return config diff --git a/src/runners/__init__.py b/src/runners/__init__.py new file mode 100644 index 0000000..c5c1306 --- /dev/null +++ b/src/runners/__init__.py @@ -0,0 +1,7 @@ +REGISTRY = {} + +from .episode_runner import EpisodeRunner +REGISTRY["episode"] = EpisodeRunner + +from .parallel_runner import ParallelRunner +REGISTRY["parallel"] = ParallelRunner diff --git a/src/runners/episode_runner.py b/src/runners/episode_runner.py new file mode 100644 index 0000000..40a7c84 --- /dev/null +++ b/src/runners/episode_runner.py @@ -0,0 +1,146 @@ +from envs import REGISTRY as env_REGISTRY +from functools import partial +from components.episode_buffer import EpisodeBatch +import numpy as np +import time + + +class EpisodeRunner: + + def __init__(self, args, logger): + self.args = args + self.logger = logger + self.batch_size = self.args.batch_size_run + if self.batch_size > 1: + self.batch_size = 1 + logger.console_logger.warning("Reset the `batch_size_run' to 1...") + + self.env = env_REGISTRY[self.args.env](**self.args.env_args) + if self.args.evaluate: + print("Waiting the environment to start...") + time.sleep(5) + self.episode_limit = self.env.episode_limit + self.t = 0 + + self.t_env = 0 + + self.train_returns = [] + self.test_returns = [] + self.train_stats = {} + self.test_stats = {} + + # Log the first run + self.log_train_stats_t = -1000000 + + def setup(self, scheme, groups, preprocess, mac): + if self.args.use_cuda and not self.args.cpu_inference: + self.batch_device = self.args.device + else: + self.batch_device = "cpu" if self.args.buffer_cpu_only else self.args.device + print(" &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& self.batch_device={}".format( + self.batch_device)) + self.new_batch = partial(EpisodeBatch, scheme, groups, self.batch_size, self.episode_limit + 1, + preprocess=preprocess, device=self.batch_device) + self.mac = mac + + def get_env_info(self): + return self.env.get_env_info() + + def save_replay(self): + self.env.save_replay() + + def close_env(self): + self.env.close() + + def reset(self): + self.batch = self.new_batch() + if (self.args.use_cuda and self.args.cpu_inference) and str(self.mac.get_device()) != "cpu": + self.mac.cpu() # copy model to cpu + + self.env.reset() + self.t = 0 + + def run(self, test_mode=False): + self.reset() + + terminated = False + episode_return = 0 + self.mac.init_hidden(batch_size=self.batch_size) + + while not terminated: + pre_transition_data = { + "state": [self.env.get_state()], + "avail_actions": [self.env.get_avail_actions()], + "obs": [self.env.get_obs()] + } + self.batch.update(pre_transition_data, ts=self.t) + + # Pass the entire batch of experiences up till now to the agents + # Receive the actions for each agent at this timestep in a batch of size 1 + actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, test_mode=test_mode) + # Fix memory leak + cpu_actions = actions.to("cpu").numpy() + + reward, terminated, env_info = self.env.step(actions[0]) + episode_return += reward + + post_transition_data = { + "actions": cpu_actions, + "reward": [(reward,)], + "terminated": [(terminated != env_info.get("episode_limit", False),)], + } + + self.batch.update(post_transition_data, ts=self.t) + + if self.args.evaluate: + time.sleep(1) + print(self.t, post_transition_data["reward"]) + + self.t += 1 + + last_data = { + "state": [self.env.get_state()], + "avail_actions": [self.env.get_avail_actions()], + "obs": [self.env.get_obs()] + } + self.batch.update(last_data, ts=self.t) + + # Select actions in the last stored state + actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, test_mode=test_mode) + # Fix memory leak + cpu_actions = actions.to("cpu").numpy() + self.batch.update({"actions": cpu_actions}, ts=self.t) + + cur_stats = self.test_stats if test_mode else self.train_stats + cur_returns = self.test_returns if test_mode else self.train_returns + log_prefix = "test_" if test_mode else "" + cur_stats.update({k: cur_stats.get(k, 0) + env_info.get(k, 0) for k in set(cur_stats) | set(env_info)}) + cur_stats["n_episodes"] = 1 + cur_stats.get("n_episodes", 0) + cur_stats["ep_length"] = self.t + cur_stats.get("ep_length", 0) + + if not test_mode: + self.t_env += self.t + + cur_returns.append(episode_return) + + if test_mode and (len(self.test_returns) == self.args.test_nepisode): + self._log(cur_returns, cur_stats, log_prefix) + elif not test_mode and self.t_env - self.log_train_stats_t >= self.args.runner_log_interval: + self._log(cur_returns, cur_stats, log_prefix) + if hasattr(self.mac.action_selector, "epsilon"): + self.logger.log_stat("epsilon", self.mac.action_selector.epsilon, self.t_env) + self.log_train_stats_t = self.t_env + + return self.batch + + def _log(self, returns, stats, prefix): + self.logger.log_stat(prefix + "return_min", np.min(returns), self.t_env) + self.logger.log_stat(prefix + "return_max", np.max(returns), self.t_env) + self.logger.log_stat(prefix + "return_mean", np.mean(returns), self.t_env) + self.logger.log_stat(prefix + "return_std", np.std(returns), self.t_env) + returns.clear() + + for k, v in stats.items(): + if k != "n_episodes": + self.logger.log_stat(prefix + k + "_mean", v / stats["n_episodes"], self.t_env) + stats.clear() diff --git a/src/runners/parallel_runner.py b/src/runners/parallel_runner.py new file mode 100644 index 0000000..ab6e04a --- /dev/null +++ b/src/runners/parallel_runner.py @@ -0,0 +1,310 @@ +from envs import REGISTRY as env_REGISTRY +from functools import partial +from components.episode_buffer import EpisodeBatch +from multiprocessing import Pipe, Process + +import numpy as np +import time + + +# Based (very) heavily on SubprocVecEnv from OpenAI Baselines +# https://github.com/openai/baselines/blob/master/baselines/common/vec_env/subproc_vec_env.py +class ParallelRunner: + + def __init__(self, args, logger): + self.args = args + self.logger = logger + self.batch_size = self.args.batch_size_run + + # Make subprocesses for the envs + self.parent_conns, self.worker_conns = zip(*[Pipe() for _ in range(self.batch_size)]) + env_fn = env_REGISTRY[self.args.env] + self.ps = [] + for i, worker_conn in enumerate(self.worker_conns): + ps = Process(target=env_worker, + args=(worker_conn, CloudpickleWrapper(partial(env_fn, **self.args.env_args)))) + self.ps.append(ps) + if self.args.evaluate: + print("Waiting the environment to start...") + time.sleep(5) + + for p in self.ps: + p.daemon = True + p.start() + + self.parent_conns[0].send(("get_env_info", None)) + self.env_info = self.parent_conns[0].recv() + self.episode_limit = self.env_info["episode_limit"] + + self.t = 0 + + self.t_env = 0 + + self.train_returns = [] + self.test_returns = [] + self.train_stats = {} + self.test_stats = {} + + self.log_train_stats_t = -100000 + + def setup(self, scheme, groups, preprocess, mac): + if self.args.use_cuda and not self.args.cpu_inference: + self.batch_device = self.args.device + else: + self.batch_device = "cpu" if self.args.buffer_cpu_only else self.args.device + #print(" &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& self.batch_device={}".format( + # self.batch_device)) + self.new_batch = partial(EpisodeBatch, scheme, groups, self.batch_size, self.episode_limit + 1, + preprocess=preprocess, device=self.batch_device) + self.mac = mac + self.scheme = scheme + self.groups = groups + self.preprocess = preprocess + + def get_env_info(self): + return self.env_info + + def save_replay(self): + pass + + def close_env(self): + for parent_conn in self.parent_conns: + parent_conn.send(("close", None)) + + def reset(self): + self.batch = self.new_batch() + + if (self.args.use_cuda and self.args.cpu_inference) and str(self.mac.get_device()) != "cpu": + self.mac.cpu() # copy model to cpu + + # Reset the envs + for parent_conn in self.parent_conns: + parent_conn.send(("reset", None)) + + pre_transition_data = { + "state": [], + "avail_actions": [], + "obs": [] + } + # Get the obs, state and avail_actions back + for parent_conn in self.parent_conns: + data = parent_conn.recv() + pre_transition_data["state"].append(data["state"]) + pre_transition_data["avail_actions"].append(data["avail_actions"]) + pre_transition_data["obs"].append(data["obs"]) + + self.batch.update(pre_transition_data, ts=0, mark_filled=True) + + self.t = 0 + self.env_steps_this_run = 0 + + def run(self, test_mode=False): + self.reset() + + all_terminated = False + episode_returns = [0 for _ in range(self.batch_size)] + episode_lengths = [0 for _ in range(self.batch_size)] + self.mac.init_hidden(batch_size=self.batch_size) + terminated = [False for _ in range(self.batch_size)] + envs_not_terminated = [b_idx for b_idx, termed in enumerate(terminated) if not termed] + final_env_infos = [] # may store extra stats like battle won. this is filled in ORDER OF TERMINATION + + save_probs = getattr(self.args, "save_probs", False) + while True: + # Pass the entire batch of experiences up till now to the agents + # Receive the actions for each agent at this timestep in a batch for each un-terminated env + if save_probs: + actions, probs = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, + bs=envs_not_terminated, test_mode=test_mode) + else: + actions = self.mac.select_actions(self.batch, t_ep=self.t, t_env=self.t_env, bs=envs_not_terminated, + test_mode=test_mode) + + cpu_actions = actions.to("cpu").numpy() + + # Update the actions taken + actions_chosen = { + "actions": np.expand_dims(cpu_actions, axis=1), + } + if save_probs: + actions_chosen["probs"] = probs.unsqueeze(1).to("cpu") + + self.batch.update(actions_chosen, bs=envs_not_terminated, ts=self.t, mark_filled=False) + + # Send actions to each env + action_idx = 0 + for idx, parent_conn in enumerate(self.parent_conns): + if idx in envs_not_terminated: # We produced actions for this env + if not terminated[idx]: # Only send the actions to the env if it hasn't terminated + parent_conn.send(("step", cpu_actions[action_idx])) + action_idx += 1 # actions is not a list over every env + + # # Update envs_not_terminated + # envs_not_terminated = [b_idx for b_idx, termed in enumerate(terminated) if not termed] + # all_terminated = all(terminated) + # if all_terminated: + # break + + # Post step data we will insert for the current timestep + post_transition_data = { + "reward": [], + "terminated": [] + } + # Data for the next step we will insert in order to select an action + pre_transition_data = { + "state": [], + "avail_actions": [], + "obs": [] + } + # Receive data back for each unterminated env + for idx, parent_conn in enumerate(self.parent_conns): + if not terminated[idx]: + data = parent_conn.recv() + # Remaining data for this current timestep + post_transition_data["reward"].append((data["reward"],)) + + episode_returns[idx] += data["reward"] + episode_lengths[idx] += 1 + if not test_mode: + self.env_steps_this_run += 1 + + env_terminated = False + if data["terminated"]: + final_env_infos.append(data["info"]) + if data["terminated"] and not data["info"].get("episode_limit", False): + env_terminated = True + terminated[idx] = data["terminated"] + post_transition_data["terminated"].append((env_terminated,)) + + # Data for the next timestep needed to select an action + pre_transition_data["state"].append(data["state"]) + pre_transition_data["avail_actions"].append(data["avail_actions"]) + pre_transition_data["obs"].append(data["obs"]) + + # Add post_transiton data into the batch + self.batch.update(post_transition_data, bs=envs_not_terminated, ts=self.t, mark_filled=False) + + if self.args.evaluate: + assert self.batch_size == 1 + move = [["北", "南", "东", "西"][action - 2] if action > 1 and action < 6 else "action-{}".format(action) + for action in cpu_actions[0]] + print(self.t, move, post_transition_data["reward"]) + time.sleep(1) + + # Move onto the next timestep + self.t += 1 + + # Add the pre-transition data + self.batch.update(pre_transition_data, bs=envs_not_terminated, ts=self.t, mark_filled=True) + + # Update envs_not_terminated + envs_not_terminated = [b_idx for b_idx, termed in enumerate(terminated) if not termed] + all_terminated = all(terminated) + if all_terminated: + break + + if not test_mode: + self.t_env += self.env_steps_this_run + + # Get stats back for each env + for parent_conn in self.parent_conns: + parent_conn.send(("get_stats", None)) + + env_stats = [] + for parent_conn in self.parent_conns: + env_stat = parent_conn.recv() + env_stats.append(env_stat) + + cur_stats = self.test_stats if test_mode else self.train_stats + cur_returns = self.test_returns if test_mode else self.train_returns + log_prefix = "test_" if test_mode else "" + infos = [cur_stats] + final_env_infos + + cur_stats.update({k: sum(d.get(k, 0) for d in infos) for k in set.union(*[set(d) for d in infos])}) + cur_stats["n_episodes"] = self.batch_size + cur_stats.get("n_episodes", 0) + cur_stats["ep_length"] = sum(episode_lengths) + cur_stats.get("ep_length", 0) + + cur_returns.extend(episode_returns) + + n_test_runs = max(1, self.args.test_nepisode // self.batch_size) * self.batch_size + if test_mode and (len(self.test_returns) == n_test_runs): + self._log(cur_returns, cur_stats, log_prefix) + elif not test_mode and self.t_env - self.log_train_stats_t >= self.args.runner_log_interval: + self._log(cur_returns, cur_stats, log_prefix) + if hasattr(self.mac.action_selector, "epsilon"): + self.logger.log_stat("epsilon", self.mac.action_selector.epsilon, self.t_env) + self.log_train_stats_t = self.t_env + + return self.batch + # return clear_no_reward_sub_trajectory(self.batch) + + def _log(self, returns, stats, prefix): + self.logger.log_stat(prefix + "return_min", np.min(returns), self.t_env) + self.logger.log_stat(prefix + "return_max", np.max(returns), self.t_env) + self.logger.log_stat(prefix + "return_mean", np.mean(returns), self.t_env) + self.logger.log_stat(prefix + "return_std", np.std(returns), self.t_env) + returns.clear() + + for k, v in stats.items(): + if k != "n_episodes": + self.logger.log_stat(prefix + k + "_mean", v / stats["n_episodes"], self.t_env) + stats.clear() + + +def env_worker(remote, env_fn): + # Make environment + env = env_fn.x() + while True: + cmd, data = remote.recv() + if cmd == "step": + actions = data + # Take a step in the environment + reward, terminated, env_info = env.step(actions) + # Return the observations, avail_actions and state to make the next action + state = env.get_state() + avail_actions = env.get_avail_actions() + obs = env.get_obs() + remote.send({ + # Data for the next timestep needed to pick an action + "state": state, + "avail_actions": avail_actions, + "obs": obs, + # Rest of the data for the current timestep + "reward": reward, + "terminated": terminated, + "info": env_info + }) + elif cmd == "reset": + env.reset() + remote.send({ + "state": env.get_state(), + "avail_actions": env.get_avail_actions(), + "obs": env.get_obs() + }) + elif cmd == "close": + env.close() + remote.close() + break + elif cmd == "get_env_info": + remote.send(env.get_env_info()) + elif cmd == "get_stats": + remote.send(env.get_stats()) + else: + raise NotImplementedError + + +class CloudpickleWrapper(): + """ + Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) + """ + + def __init__(self, x): + self.x = x + + def __getstate__(self): + import cloudpickle + return cloudpickle.dumps(self.x) + + def __setstate__(self, ob): + import pickle + self.x = pickle.loads(ob) diff --git a/src/utils/data_processing.py b/src/utils/data_processing.py new file mode 100644 index 0000000..317cb6d --- /dev/null +++ b/src/utils/data_processing.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python +# -*- coding: UTF-8 -*- + +from components.episode_buffer import EpisodeBatch +import copy +import numpy as np +import torch as th + + +def clear_no_reward_sub_trajectory(batch): + """ + :param batch: + :return: + """ + filled = batch.data.transition_data["filled"] # [bs, traj_length, 1] + rewards = batch.data.transition_data["reward"] # [bs, traj_length, 1] + bs, traj_length = filled.shape[0], filled.shape[1] + fixed_row = [] + for t in range(traj_length - 1, 0, -1): + remained_rows = [i for i in range(0, bs) if i not in fixed_row] + for row_idx in remained_rows: + if rewards[row_idx, t - 1, 0] == 0: # no reward + filled[row_idx, t, 0] = 0 + if t == 1: + filled[row_idx, t - 1, 0] = 0 # the trajectory's Return is 0. + else: # receive reward + fixed_row.append(row_idx) + + return batch[fixed_row] + + +def _get_obs_component_dim(args): + move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim = args.obs_component # [4, (6, 5), (4, 5), 1] + enemy_feats_dim = np.prod(enemy_feats_dim) + ally_feats_dim = np.prod(ally_feats_dim) + return move_feats_dim, enemy_feats_dim, ally_feats_dim, own_feats_dim + + +def _generate_permutation_matrix(bs, seq_length, n_agents, N, device): + permutation_matrix = th.zeros(size=[bs, seq_length, n_agents, N, N], dtype=th.float32, device=device) + ordered_list = np.arange(N) # [0, 1, 2, 3, ...] + shuffled_list = ordered_list.copy() + np.random.shuffle(shuffled_list) # [3, 0, 2, 1, ...] + permutation_matrix[:, :, :, ordered_list, shuffled_list] = 1 + return permutation_matrix + + +def do_data_augmentation(args, batch: EpisodeBatch, augment_times=2): + """ + 'obs', 'attack action' and 'available action' need to be transformed + :param args: + :param batch: + :param augment_times: + :return: + """ + bs = batch.batch_size + seq_length = batch.max_seq_length + obs_component_dim = _get_obs_component_dim(args=args) + attack_action_start_idx = 6 + + augmented_data = [] + for t in range(augment_times): + new_batch = copy.deepcopy(batch) + obs = new_batch.data.transition_data["obs"] # [bs, seq_length, n_agents, obs_dim] + # actions = new_batch.data.transition_data["actions"] # [bs, seq_length, n_agents, 1] + actions_onehot = new_batch.data.transition_data["actions_onehot"] # [bs, seq_length, n_agents, action_num] + avail_actions = new_batch.data.transition_data["avail_actions"] # [bs, seq_length, n_agents, action_num] + + # (1) split observation according to the semantic meaning + move_feats, enemy_feats, ally_feats, own_feats = th.split(obs, obs_component_dim, dim=-1) + reshaped_enemy_feats = enemy_feats.contiguous().view(bs, seq_length, args.n_agents, args.n_enemies, -1) + reshaped_ally_feats = ally_feats.contiguous().view(bs, seq_length, args.n_agents, (args.n_agents - 1), -1) + + # (2) split available action into 2 groups: 'move' and 'attack'. + avail_other_action = avail_actions[:, :, :, :attack_action_start_idx] # (no_op, stop, up, down, right, left) + avail_attack_action = avail_actions[:, :, :, attack_action_start_idx:] # [n_enemies] + + # (3) split actions_onehot into 2 groups: 'move' and 'attack'. + other_action_onehot = actions_onehot[:, :, :, :attack_action_start_idx] # (no_op, stop, up, down, right, left) + attack_action_onehot = actions_onehot[:, :, :, attack_action_start_idx:] # [n_enemies] + + # (4) generate permutation matrix for 'ally' and 'enemy' + ally_perm_matrix = _generate_permutation_matrix(bs, seq_length, args.n_agents, args.n_agents - 1, + device=obs.device) + enemy_perm_matrix = _generate_permutation_matrix(bs, seq_length, args.n_agents, args.n_enemies, + device=obs.device) + + # (5) permute obs: including ally and enemy + # [bs, seq_length, n_agents, N, N] * [bs, seq_length, n_agents, N, feature_dim] + permuted_enemy_feat = th.matmul(enemy_perm_matrix, reshaped_enemy_feats).view(bs, seq_length, args.n_agents, -1) + permuted_ally_feat = th.matmul(ally_perm_matrix, reshaped_ally_feats).view(bs, seq_length, args.n_agents, -1) + permuted_obs = th.cat([move_feats, permuted_enemy_feat, permuted_ally_feat, own_feats], dim=-1) + # permuted_obs = th.cat([move_feats, permuted_enemy_feat, ally_feats, own_feats], dim=-1) + + # (6) permute available action (use the same permutation matrix for enemy) + permuted_avail_attack_action = th.matmul(enemy_perm_matrix, avail_attack_action.unsqueeze(-1).float()).view( + bs, seq_length, args.n_agents, -1) + permuted_avail_actions = th.cat([avail_other_action, permuted_avail_attack_action.int()], dim=-1) + + # (7) permute attack_action_onehot (use the same permutation matrix for enemy) + # used when obs_last_action is True + permuted_attack_action_onehot = th.matmul(enemy_perm_matrix, attack_action_onehot.unsqueeze(-1).float()).view( + bs, seq_length, args.n_agents, -1) + permuted_action_onehot = th.cat([other_action_onehot, permuted_attack_action_onehot], dim=-1) + permuted_action = permuted_action_onehot.max(dim=-1, keepdim=True)[1] + + new_batch.data.transition_data["obs"] = permuted_obs + new_batch.data.transition_data["actions"] = permuted_action + new_batch.data.transition_data["actions_onehot"] = permuted_action_onehot + new_batch.data.transition_data["avail_actions"] = permuted_avail_actions + + if augment_times > 1: + augmented_data.append(new_batch) + if augment_times > 1: + return augmented_data + return new_batch diff --git a/src/utils/dict2namedtuple.py b/src/utils/dict2namedtuple.py new file mode 100644 index 0000000..bb474fd --- /dev/null +++ b/src/utils/dict2namedtuple.py @@ -0,0 +1,5 @@ +from collections import namedtuple + + +def convert(dictionary): + return namedtuple('GenericDict', dictionary.keys())(**dictionary) diff --git a/src/utils/logging.py b/src/utils/logging.py new file mode 100644 index 0000000..5393b7f --- /dev/null +++ b/src/utils/logging.py @@ -0,0 +1,68 @@ +from collections import defaultdict +import logging +import numpy as np +import torch as th + +class Logger: + def __init__(self, console_logger): + self.console_logger = console_logger + + self.use_tb = False + self.use_sacred = False + self.use_hdf = False + + self.stats = defaultdict(lambda: []) + + def setup_tb(self, directory_name): + # Import here so it doesn't have to be installed if you don't use it + from tensorboard_logger import configure, log_value + configure(directory_name) + self.tb_logger = log_value + self.use_tb = True + + def setup_sacred(self, sacred_run_dict): + self.sacred_info = sacred_run_dict.info + self.use_sacred = True + + def log_stat(self, key, value, t, to_sacred=True): + self.stats[key].append((t, value)) + + if self.use_tb: + self.tb_logger(key, value, t) + + if self.use_sacred and to_sacred: + if key in self.sacred_info: + self.sacred_info["{}_T".format(key)].append(t) + self.sacred_info[key].append(value) + else: + self.sacred_info["{}_T".format(key)] = [t] + self.sacred_info[key] = [value] + + def print_recent_stats(self): + log_str = "Recent Stats | t_env: {:>10} | Episode: {:>8}\n".format(*self.stats["episode"][-1]) + i = 0 + for (k, v) in sorted(self.stats.items()): + if k == "episode": + continue + i += 1 + window = 5 if k != "epsilon" else 1 + item = "{:.4f}".format(th.mean(th.tensor([float(x[1]) for x in self.stats[k][-window:]]))) + log_str += "{:<25}{:>8}".format(k + ":", item) + log_str += "\n" if i % 4 == 0 else "\t" + self.console_logger.info(log_str) + # Reset stats to avoid accumulating logs in memory + self.stats = defaultdict(lambda: []) + + +# set up a custom logger +def get_logger(): + logger = logging.getLogger() + logger.handlers = [] + ch = logging.StreamHandler() + formatter = logging.Formatter('[%(levelname)s %(asctime)s] %(name)s %(message)s', '%H:%M:%S') + ch.setFormatter(formatter) + logger.addHandler(ch) + logger.setLevel('DEBUG') + + return logger + diff --git a/src/utils/noisy_liner.py b/src/utils/noisy_liner.py new file mode 100644 index 0000000..36b191e --- /dev/null +++ b/src/utils/noisy_liner.py @@ -0,0 +1,78 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class NoisyLinear(nn.Module): + r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` + This module supports :ref:`TensorFloat32`. + Args: + in_features: size of each input sample + out_features: size of each output sample + bias: If set to ``False``, the layer will not learn an additive bias. + Default: ``True`` + Shape: + - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of + additional dimensions and :math:`H_{in} = \text{in\_features}` + - Output: :math:`(N, *, H_{out})` where all but the last dimension + are the same shape as the input and :math:`H_{out} = \text{out\_features}`. + Attributes: + weight: the learnable weights of the module of shape + :math:`(\text{out\_features}, \text{in\_features})`. The values are + initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where + :math:`k = \frac{1}{\text{in\_features}}` + bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. + If :attr:`bias` is ``True``, the values are initialized from + :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where + :math:`k = \frac{1}{\text{in\_features}}` + Examples:: + >>> m = nn.Linear(20, 30) + >>> input = torch.randn(128, 20) + >>> output = m(input) + >>> print(output.size()) + torch.Size([128, 30]) + """ + __constants__ = ['in_features', 'out_features'] + # in_features: int + # out_features: int + # weight: torch.Tensor + + def __init__(self, in_features: int, out_features: int, bias: bool = True, device: str = 'cpu') -> None: + super(NoisyLinear, self).__init__() + self.in_features = in_features + self.out_features = out_features + self.bias = bias + self.device = device + self.u_w = nn.Parameter(torch.Tensor(out_features, in_features)) + self.s_w = nn.Parameter(torch.Tensor(out_features, in_features)) + if bias: + self.u_b = nn.Parameter(torch.Tensor(out_features)) + self.s_b = nn.Parameter(torch.Tensor(out_features)) + else: + self.register_parameter('bias', None) + self.reset_parameters() + + def reset_parameters(self) -> None: + nn.init.kaiming_uniform_(self.u_w, a=math.sqrt(3 / self.in_features)) + nn.init.constant_(self.s_w, 0.017) + if self.bias is not None: + nn.init.uniform_(self.u_b, a=math.sqrt(3 / self.in_features)) + nn.init.constant_(self.s_b, 0.017) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + if self.training: + e_w = torch.randn(self.s_w.shape, device=self.device) + e_b = torch.randn(self.s_b.shape, device=self.device) + weight = self.u_w + (self.s_w * e_w) + bias = self.u_b + (self.s_b * e_b) + else: + weight = self.u_w + bias = self.u_b + return F.linear(input, weight, bias) + + def extra_repr(self) -> str: + return 'in_features={}, out_features={}, bias={}'.format( + self.in_features, self.out_features, self.bias is not None + ) \ No newline at end of file diff --git a/src/utils/rl_utils.py b/src/utils/rl_utils.py new file mode 100644 index 0000000..397cb32 --- /dev/null +++ b/src/utils/rl_utils.py @@ -0,0 +1,89 @@ +import torch as th +import torch.nn as nn +import numpy as np + + +def build_td_lambda_targets(rewards, terminated, mask, target_qs, gamma, td_lambda): + # Assumes in B*T*A and , , in (at least) B*T-1*1 + # Initialise last lambda -return for not terminated episodes + ret = target_qs.new_zeros(*target_qs.shape) + ret[:, -1] = target_qs[:, -1] * (1 - th.sum(terminated, dim=1)) + # Backwards recursive update of the "forward view" + for t in range(ret.shape[1] - 2, -1, -1): + ret[:, t] = td_lambda * gamma * ret[:, t + 1] + mask[:, t] \ + * (rewards[:, t] + (1 - td_lambda) * gamma * target_qs[:, t + 1] * (1 - terminated[:, t])) + # Returns lambda-return from t=0 to t=T-1, i.e. in B*T-1*A + return ret[:, 0:-1] + + +def build_gae_targets(rewards, masks, values, gamma, lambd): + B, T, A, _ = values.size() + T-=1 + advantages = th.zeros(B, T, A, 1).to(device=values.device) + advantage_t = th.zeros(B, A, 1).to(device=values.device) + + for t in reversed(range(T)): + delta = rewards[:, t] + values[:, t+1] * gamma * masks[:, t] - values[:, t] + advantage_t = delta + advantage_t * gamma * lambd * masks[:, t] + advantages[:, t] = advantage_t + + returns = values[:, :T] + advantages + return advantages, returns + + +def build_q_lambda_targets(rewards, terminated, mask, exp_qvals, qvals, gamma, td_lambda): + # Assumes in B*T*A and , , in (at least) B*T-1*1 + # Initialise last lambda -return for not terminated episodes + ret = exp_qvals.new_zeros(*exp_qvals.shape) + ret[:, -1] = exp_qvals[:, -1] * (1 - th.sum(terminated, dim=1)) + # Backwards recursive update of the "forward view" + for t in range(ret.shape[1] - 2, -1, -1): + reward = rewards[:, t] + exp_qvals[:, t] - qvals[:, t] #off-policy correction + ret[:, t] = td_lambda * gamma * ret[:, t + 1] + mask[:, t] \ + * (reward + (1 - td_lambda) * gamma * exp_qvals[:, t + 1] * (1 - terminated[:, t])) + # Returns lambda-return from t=0 to t=T-1, i.e. in B*T-1*A + return ret[:, 0:-1] + + +def build_target_q(td_q, target_q, mac, mask, gamma, td_lambda, n): + aug = th.zeros_like(td_q[:, :1]) + + #Tree diagram + mac = mac[:, :-1] + tree_q_vals = th.zeros_like(td_q) + coeff = 1.0 + t1 = td_q[:] + for _ in range(n): + tree_q_vals += t1 * coeff + t1 = th.cat(((t1 * mac)[:, 1:], aug), dim=1) + coeff *= gamma * td_lambda + return target_q + tree_q_vals + +class RunningMeanStd(object): + # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + def __init__(self, epsilon=1e-4, shape=()): + self.mean = np.zeros(shape, 'float64') + self.var = np.ones(shape, 'float64') + self.count = epsilon + + def update(self, x): + batch_mean = np.mean(x, axis=0) + batch_var = np.var(x, axis=0) + batch_count = x.shape[0] + self.update_from_moments(batch_mean, batch_var, batch_count) + + def update_from_moments(self, batch_mean, batch_var, batch_count): + delta = batch_mean - self.mean + tot_count = self.count + batch_count + + new_mean = self.mean + delta * batch_count / tot_count + m_a = self.var * (self.count) + m_b = batch_var * (batch_count) + M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count) + new_var = M2 / (self.count + batch_count) + + new_count = batch_count + self.count + + self.mean = new_mean + self.var = new_var + self.count = new_count \ No newline at end of file diff --git a/src/utils/th_utils.py b/src/utils/th_utils.py new file mode 100644 index 0000000..db2356a --- /dev/null +++ b/src/utils/th_utils.py @@ -0,0 +1,33 @@ +import torch +from torch import nn + +def clip_by_tensor(t,t_min,t_max): + """ + clip_by_tensor + :param t: tensor + :param t_min: min + :param t_max: max + :return: cliped tensor + """ + t=t.float() + t_min=t_min.float() + t_max=t_max.float() + + result = (t >= t_min).float() * t + (t < t_min).float() * t_min + result = (result <= t_max).float() * result + (result > t_max).float() * t_max + return result + +def get_parameters_num(param_list): + return str(sum(p.numel() for p in param_list) / 1000) + 'K' + + +def init(module, weight_init, bias_init, gain=1): + weight_init(module.weight.data, gain=gain) + bias_init(module.bias.data) + return module + + +def orthogonal_init_(m, gain=1): + if isinstance(m, nn.Linear): + init(m, nn.init.orthogonal_, + lambda x: nn.init.constant_(x, 0), gain=gain) \ No newline at end of file diff --git a/src/utils/timehelper.py b/src/utils/timehelper.py new file mode 100644 index 0000000..a168c79 --- /dev/null +++ b/src/utils/timehelper.py @@ -0,0 +1,43 @@ +import time +import numpy as np + + +def print_time(start_time, T, t_max, episode, episode_rewards): + time_elapsed = time.time() - start_time + T = max(1, T) + time_left = time_elapsed * (t_max - T) / T + # Just in case its over 100 days + time_left = min(time_left, 60 * 60 * 24 * 100) + last_reward = "N\A" + if len(episode_rewards) > 5: + last_reward = "{:.2f}".format(np.mean(episode_rewards[-50:])) + print("\033[F\033[F\x1b[KEp: {:,}, T: {:,}/{:,}, Reward: {}, \n\x1b[KElapsed: {}, Left: {}\n".format(episode, T, t_max, last_reward, time_str(time_elapsed), time_str(time_left)), " " * 10, end="\r") + + +def time_left(start_time, t_start, t_current, t_max): + if t_current >= t_max: + return "-" + time_elapsed = time.time() - start_time + t_current = max(1, t_current) + time_left = time_elapsed * (t_max - t_current) / (t_current - t_start) + # Just in case its over 100 days + time_left = min(time_left, 60 * 60 * 24 * 100) + return time_str(time_left) + + +def time_str(s): + """ + Convert seconds to a nicer string showing days, hours, minutes and seconds + """ + days, remainder = divmod(s, 60 * 60 * 24) + hours, remainder = divmod(remainder, 60 * 60) + minutes, seconds = divmod(remainder, 60) + string = "" + if days > 0: + string += "{:d} days, ".format(int(days)) + if hours > 0: + string += "{:d} hours, ".format(int(hours)) + if minutes > 0: + string += "{:d} minutes, ".format(int(minutes)) + string += "{:d} seconds".format(int(seconds)) + return string diff --git a/src/utils/value_norm.py b/src/utils/value_norm.py new file mode 100644 index 0000000..f1170ba --- /dev/null +++ b/src/utils/value_norm.py @@ -0,0 +1,76 @@ +import numpy as np + +import torch +import torch.nn as nn + + +class ValueNorm(nn.Module): + """ Normalize a vector of observations - across the first norm_axes dimensions""" + + def __init__(self, input_shape, norm_axes=1, beta=0.99999, per_element_update=False, epsilon=1e-5, device=torch.device("cpu")): + super(ValueNorm, self).__init__() + + self.input_shape = input_shape + self.norm_axes = norm_axes + self.epsilon = epsilon + self.beta = beta + self.per_element_update = per_element_update + self.tpdv = dict(dtype=torch.float32, device=device) + + self.running_mean = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) + self.running_mean_sq = nn.Parameter(torch.zeros(input_shape), requires_grad=False).to(**self.tpdv) + self.debiasing_term = nn.Parameter(torch.tensor(0.0), requires_grad=False).to(**self.tpdv) + + self.reset_parameters() + + def reset_parameters(self): + self.running_mean.zero_() + self.running_mean_sq.zero_() + self.debiasing_term.zero_() + + def running_mean_var(self): + debiased_mean = self.running_mean / self.debiasing_term.clamp(min=self.epsilon) + debiased_mean_sq = self.running_mean_sq / self.debiasing_term.clamp(min=self.epsilon) + debiased_var = (debiased_mean_sq - debiased_mean ** 2).clamp(min=1e-2) + return debiased_mean, debiased_var + + @torch.no_grad() + def update(self, input_vector): + if type(input_vector) == np.ndarray: + input_vector = torch.from_numpy(input_vector) + input_vector = input_vector.to(**self.tpdv) + + batch_mean = input_vector.mean(dim=tuple(range(self.norm_axes))) + batch_sq_mean = (input_vector ** 2).mean(dim=tuple(range(self.norm_axes))) + + if self.per_element_update: + batch_size = np.prod(input_vector.size()[:self.norm_axes]) + weight = self.beta ** batch_size + else: + weight = self.beta + + self.running_mean.mul_(weight).add_(batch_mean * (1.0 - weight)) + self.running_mean_sq.mul_(weight).add_(batch_sq_mean * (1.0 - weight)) + self.debiasing_term.mul_(weight).add_(1.0 * (1.0 - weight)) + + def normalize(self, input_vector): + # Make sure input is float32 + if type(input_vector) == np.ndarray: + input_vector = torch.from_numpy(input_vector) + input_vector = input_vector.to(**self.tpdv) + + mean, var = self.running_mean_var() + out = (input_vector - mean[(None,) * self.norm_axes]) / torch.sqrt(var)[(None,) * self.norm_axes] + + return out + + def denormalize(self, input_vector): + """ Transform normalized data back into original distribution """ + if type(input_vector) == np.ndarray: + input_vector = torch.from_numpy(input_vector) + input_vector = input_vector.to(**self.tpdv) + + mean, var = self.running_mean_var() + out = input_vector * torch.sqrt(var)[(None,) * self.norm_axes] + mean[(None,) * self.norm_axes] + + return out \ No newline at end of file

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