diff --git a/.gitignore b/.gitignore
index ab3e8ce..1dbb238 100644
--- a/.gitignore
+++ b/.gitignore
@@ -162,3 +162,17 @@ cython_debug/
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
+
+
+# 3rdparty/
+# __MACOSX/
+# __pycache__/
+# 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 @@
+
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diff --git a/README.md b/README.md
index dbc91e5..84a9951 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,257 @@
-# pymarl3
+# [pymarl3](https://github.com/tjuHaoXiaotian/pymarl3): the source code of the ICLR-2023 paper
+**[Boosting Multi-Agent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks](https://openreview.net/pdf?id=OxNQXyZK-K8)**.
+
+We extend [**pymarl2** (https://github.com/hijkzzz/pymarl2)](https://github.com/hijkzzz/pymarl2) to **pymarl3**, adding the support for the [SMAC-V2 environment](https://github.com/oxwhirl/smacv2) and equipping the MARL algorithms with permutation invariance and permutation equivariance properties.
+
+## Key Features:
+* (1) **Support both [SMAC-V1](https://github.com/oxwhirl/smac) and [SMAC-V2](https://github.com/oxwhirl/smacv2)** (without the need of installing each environment separately).
+ * 
+* (2) Equip the MARL algorithms of [**pymarl2**](https://github.com/hijkzzz/pymarl2) with the **permutation invariance (PI) and permutation equivariance (PE)** properties. 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)
+
+
+
+HPN incorporates [hypernetworks](https://arxiv.org/pdf/1609.09106) to generate different
+weights s for different input
+components s to improve representational capacity while ensuring the
+same  always be assigned with the same
+weight . The architecture of our HPN is shown in the above Figure (b). We
+also take the ) 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  for
+each  of the input set . As
+shown in above Figure (b),  (which can be viewed as a batch
+of  s each of which is of
+dimension , represented by different shades of blue) is firstly fed into a
+shared hypernetwork (marked in yellow), whose input size is  and output size
+is . Then, the corresponding outputs are reshaped
+to  and serve as the submodule
+weights s of the normal FC layer (see Figure (a)). Note that
+different s will generate
+different s and the same  will
+always correspond to the same . Then,
+each  is multiplied by  and all
+multiplication results and the bias  are summed together to get the output.
+Since each element  is processed separately by its
+corresponding  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.,  of SMAC, are also generated by a
+hypernetwork. As mentioned above, the input  and
+output  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.
+
+
+
+### 2.2 Applying HPN to QPLEX and MAPPO.
+
+
+
+
+### 2.3 Comparison with baselines considering permutation invariance or permutation equivariance.
+
+
+
+### 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.
+
+
+## 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 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. 
+
+### 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.
+
+
+## 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  where  of size ) is a set consisting of  components (each of which is of dimension ), is said to be permutation invariant if permutation of input components does not change the output of the function. Mathematically, =f(M\left[x_1,x_2,\ldots\text{}x_m\right]^\mathsf{T})), where  is the permutation matrix of size ), 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  is permutation equivariant if permutation of input components permutes the output components with the same permutation . Mathematically, =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  components, including  learning agents and  non-player characters. Therefore, the states, observations are factorizable as sets of  components , where each component  represents an atomic semantic meaning (e.g., agent 's features) whose dimension is . Because shuffling the order of  components does not change the information of the set, one would expect many functions, e.g., the policy function ), possess permutation invariance and permutation equivariance. These properties can be exploited to design more efficient MARL algorithms, especially when the  components are homogeneous, i.e., semantically identical (belonging to the same type, having identical feature spaces, action spaces and reward functions).
+
+Taking ) as an example, the input is the observation , and the outputs are Q-values of all actions in . Since the  components are homogeneous, they have the same feature space, i.e., . Thus, the size of an **fixedly ordered representation** of  is . 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 . 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 ) as the example, if there is a direct correspondence between the action Q-value in output and the component in input , then ) for these actions should be permutation equivariant; otherwise, ) should be permutation invariant.
+
+
+
+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  could be divided into 2 groups: an ally group  and an enemy group . The output Q-values of the actions could be divided into 2 groups as well: Q-values for move actions , i.e., , and attack actions . Since there is a one-to-one correspondence between the elements in  and , the Q-values of  should be equivariant to the permutations of , while the Q-values of  should be invariant to the permutations of the whole set . Overall, a desired model of ) should be both permutation invariant and permutation equivariance.
+
+
+
+## 2. Model Architecture of API-HyPerNetwork (API-HPN)
+
+
+
+API-HPN incorporates [hypernetworks](https://arxiv.org/pdf/1609.09106) to generate different weights s for different input components s to improve representational capacity while ensuring the same  always be assigned with the same weight . The architecture of our API-HPN is shown in the above Figure (b). We also take the ) 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  for each  of the input set . As shown in above Figure (b),  (which can be viewed as a batch of  s each of which is of dimension , represented by different shades of blue) is firstly fed into a shared hypernetwork (marked in yellow), whose input size is  and output size is . Then, the corresponding outputs are reshaped to  and serve as the submodule weights s of the normal FC layer (see Figure (a)). Note that different s will generate different s and the same  will always correspond to the same . Then, each  is multiplied by  and all multiplication results and the bias  are summed together to get the output. Since each element  is processed separately by its corresponding  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.,  of SMAC, are also generated by a hypernetwork. As mentioned above, the input  and output  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
+
+
+
+#### 3.4.2 Comparison with baselines considering permutation invariance and permutation equivariant property
+
+
+
+#### 3.4.3 Ablation Studies
+
+
+
+
+
+
+| 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}
+}
+```
+
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diff --git a/install_dependencies.sh b/install_dependencies.sh
new file mode 100644
index 0000000..e29c33a
--- /dev/null
+++ b/install_dependencies.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+# Install PyTorch and Python Packages
+# 3. 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 b/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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diff --git a/results/sacred/10gen_protoss/feudal/1/config.json b/results/sacred/10gen_protoss/feudal/1/config.json
new file mode 100644
index 0000000..a4c4ded
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@@ -0,0 +1,131 @@
+{
+ "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,
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+ "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,
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+ "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,
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+ "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": false,
+ "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": 618959174,
+ "state_dim": 80,
+ "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_tensorboard": false,
+ "vf_coef": 0.5,
+ "worker_hidden_dim": 64
+}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/1/cout.txt b/results/sacred/10gen_protoss/feudal/1/cout.txt
new file mode 100644
index 0000000..e69de29
diff --git a/results/sacred/10gen_protoss/feudal/1/info.json b/results/sacred/10gen_protoss/feudal/1/info.json
new file mode 100644
index 0000000..f7b6ca2
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+{
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+ 0.0
+ ],
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+ 216
+ ],
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+ 5.0
+ ],
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+ 216
+ ],
+ "dead_enemies_mean": [
+ 0.0
+ ],
+ "dead_enemies_mean_T": [
+ 216
+ ],
+ "ep_length_mean": [
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+ ],
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+ ],
+ "epsilon": [
+ 1.0
+ ],
+ "epsilon_T": [
+ 216
+ ],
+ "return_max": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 4.99757281553398
+ }
+ ],
+ "return_max_T": [
+ 216
+ ],
+ "return_mean": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 3.5837944867537175
+ }
+ ],
+ "return_mean_T": [
+ 216
+ ],
+ "return_min": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 2.4034653465346536
+ }
+ ],
+ "return_min_T": [
+ 216
+ ],
+ "return_std": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 1.0627956390471756
+ }
+ ],
+ "return_std_T": [
+ 216
+ ],
+ "test_battle_won_mean": [
+ 0.0
+ ],
+ "test_battle_won_mean_T": [
+ 216
+ ],
+ "test_dead_allies_mean": [
+ 5.0
+ ],
+ "test_dead_allies_mean_T": [
+ 216
+ ],
+ "test_dead_enemies_mean": [
+ 0.96875
+ ],
+ "test_dead_enemies_mean_T": [
+ 216
+ ],
+ "test_ep_length_mean": [
+ 54.5
+ ],
+ "test_ep_length_mean_T": [
+ 216
+ ],
+ "test_return_max": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 12.280241935483874
+ }
+ ],
+ "test_return_max_T": [
+ 216
+ ],
+ "test_return_mean": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 4.9681482554589795
+ }
+ ],
+ "test_return_mean_T": [
+ 216
+ ],
+ "test_return_min": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.0
+ }
+ ],
+ "test_return_min_T": [
+ 216
+ ],
+ "test_return_std": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 3.06453424806387
+ }
+ ],
+ "test_return_std_T": [
+ 216
+ ]
+}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/1/metrics.json b/results/sacred/10gen_protoss/feudal/1/metrics.json
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/results/sacred/10gen_protoss/feudal/1/metrics.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/1/run.json b/results/sacred/10gen_protoss/feudal/1/run.json
new file mode 100644
index 0000000..5ceccc6
--- /dev/null
+++ b/results/sacred/10gen_protoss/feudal/1/run.json
@@ -0,0 +1,125 @@
+{
+ "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 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 146, in train\n cos_sim = F.cosine_similarity(temp_state_diff, temp_goals, dim=-1)\n",
+ "RuntimeError: The size of tensor a (57408) must match the size of tensor b (49920) at non-singleton dimension 0\n"
+ ],
+ "heartbeat": "2025-01-05T19:45:42.461491",
+ "host": {
+ "ENV": {},
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new file mode 100644
index 0000000..f11bb05
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new file mode 100644
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new file mode 100644
index 0000000..3907e58
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new file mode 100644
index 0000000..9e26dfe
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new file mode 100644
index 0000000..24a01e3
--- /dev/null
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new file mode 100644
index 0000000..eef8f9b
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new file mode 100644
index 0000000..e69de29
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new file mode 100644
index 0000000..78f4183
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+}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/5/cout.txt b/results/sacred/10gen_protoss/feudal/5/cout.txt
new file mode 100644
index 0000000..e69de29
diff --git a/results/sacred/10gen_protoss/feudal/5/info.json b/results/sacred/10gen_protoss/feudal/5/info.json
new file mode 100644
index 0000000..6596464
--- /dev/null
+++ b/results/sacred/10gen_protoss/feudal/5/info.json
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+ ]
+}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/5/metrics.json b/results/sacred/10gen_protoss/feudal/5/metrics.json
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/results/sacred/10gen_protoss/feudal/5/metrics.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/feudal/5/run.json b/results/sacred/10gen_protoss/feudal/5/run.json
new file mode 100644
index 0000000..83bd40b
--- /dev/null
+++ 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"
+ }
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+ ],
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+ "utils\\logging.py",
+ "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py"
+ ]
+ ]
+ },
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+ "Traceback (most recent call last):\n",
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+ " 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",
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+ ],
+ "python_version": "3.10.16"
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+ "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
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+ "--queue": false,
+ "--s3": null,
+ "--sql": null,
+ "--tiny_db": null,
+ "--unobserved": false,
+ "COMMAND": null,
+ "UPDATE": [
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+ "runner=parallel",
+ "batch_size_run=4",
+ "buffer_size=5000",
+ "t_max=4050000",
+ "epsilon_anneal_time=100000",
+ "batch_size=128",
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+ "resources": [],
+ "result": null,
+ "start_time": "2025-01-05T16:15:29.896110",
+ "status": "FAILED",
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+}
\ 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": {
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+ "--capture": null,
+ "--comment": null,
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+ },
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+ "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,
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+ "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",
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+ "load_step": 0,
+ "local_results_path": "results",
+ "log_interval": 10000,
+ "lr": 0.001,
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+ "optimizer": "adam",
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+ "return_priority": false,
+ "rnn_hidden_dim": 64,
+ "run": "default",
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+ "save_model": true,
+ "save_model_interval": 2000000,
+ "save_replay": false,
+ "seed": 788463946,
+ "t_max": 4050000,
+ "target_update_interval": 200,
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+ "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 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_00-16-48\\\\events.out.tfevents.1736093809.Taiyopen'\n"
+ ],
+ "heartbeat": "2025-01-05T16:16:49.016218",
+ "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,
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\ 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 @@
+{
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+ "checkpoint_path": "",
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+ "start_positions": {
+ "dist_type": "surrounded_and_reflect",
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+ "p": 0.5
+ },
+ "team_gen": {
+ "dist_type": "weighted_teams",
+ "exception_unit_types": [
+ "colossus"
+ ],
+ "observe": true,
+ "unit_types": [
+ "stalker",
+ "zealot",
+ "colossus"
+ ],
+ "weights": [
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+ 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",
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+ "obs_all_health": true,
+ "obs_instead_of_state": false,
+ "obs_last_action": false,
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+ "replay_prefix": "",
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+ "reward_defeat": 0,
+ "reward_negative_scale": 0.5,
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+ "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",
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+ "name": "qmix",
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+ "obs_last_action": false,
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+ "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"
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+ "meta": {
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+ "config_updates": {
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+ "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
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+}
\ 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",
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+ "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",
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+ "load_step": 0,
+ "local_results_path": "results",
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+ "lr": 0.001,
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+ "run": "default",
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+ "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",
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+ " 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"
+ },
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+ "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": true
+ },
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+ "--unobserved": false,
+ "COMMAND": null,
+ "UPDATE": [
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+ "batch_size_run=4",
+ "buffer_size=5000",
+ "t_max=4050000",
+ "epsilon_anneal_time=100000",
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+ "td_lambda=0.6",
+ "use_tensorboard=True"
+ ],
+ "help": false,
+ "with": true
+ }
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+ "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
+ },
+ "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,
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+}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/qmix/6/cout.txt b/results/sacred/10gen_protoss/qmix/6/cout.txt
new file mode 100644
index 0000000..e69de29
diff --git a/results/sacred/10gen_protoss/qmix/6/info.json b/results/sacred/10gen_protoss/qmix/6/info.json
new file mode 100644
index 0000000..f12cda6
--- /dev/null
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+ ],
+ "test_return_std_T": [
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diff --git a/results/sacred/10gen_protoss/qmix/6/metrics.json b/results/sacred/10gen_protoss/qmix/6/metrics.json
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/results/sacred/10gen_protoss/qmix/6/metrics.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/results/sacred/10gen_protoss/qmix/6/run.json b/results/sacred/10gen_protoss/qmix/6/run.json
new file mode 100644
index 0000000..6ab322a
--- /dev/null
+++ b/results/sacred/10gen_protoss/qmix/6/run.json
@@ -0,0 +1,116 @@
+{
+ "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"
+ ]
+ ]
+ },
+ "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,
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+ "save_model": true,
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+}
\ 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": {
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+ {
+ "commit": "44bb188185fd0292a1a306c86396027efb44224d",
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+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
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+ " 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",
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+ " 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",
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+ "ENV": {},
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\ 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",
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+ "buffer_size": 5000,
+ "c": 1,
+ "checkpoint_path": "",
+ "cpu_inference": true,
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+ "critic_lr": 0.0005,
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+ "env": "sc2",
+ "env_args": {
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+ "debug": false,
+ "difficulty": "7",
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+ "heuristic_ai": false,
+ "heuristic_rest": false,
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+ "obs_pathing_grid": false,
+ "obs_terrain_height": false,
+ "obs_timestep_number": false,
+ "replay_dir": "",
+ "replay_prefix": "",
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+ "reward_negative_scale": 0.5,
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+ "seed": null,
+ "state_last_action": true,
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+ },
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+}
\ 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
+ ],
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+ 150
+ ],
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+ ],
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+ 150
+ ],
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+ 0.0
+ ],
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+ ],
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+ ],
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+ 150
+ ],
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+ "dtype": "float64",
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+ "value": 0.0
+ }
+ ],
+ "test_return_max_T": [
+ 150
+ ],
+ "test_return_mean": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
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+ }
+ ],
+ "test_return_mean_T": [
+ 150
+ ],
+ "test_return_min": [
+ {
+ "dtype": "float64",
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+ "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"
+ },
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+ "commit": "44bb188185fd0292a1a306c86396027efb44224d",
+ "dirty": true,
+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
+ }
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+ [
+ "main.py",
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+ ],
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+ "utils\\logging.py",
+ "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py"
+ ]
+ ]
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+ "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",
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+ "driver_version": "560.94",
+ "gpus": [
+ {
+ "model": "NVIDIA GeForce RTX 4080 SUPER",
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+ "total_memory": 16376
+ }
+ ]
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+ "env_args": {
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+ "UPDATE": [
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+ "start_time": "2024-12-29T19:02:24.077301",
+ "status": "FAILED",
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+}
\ No newline at end of file
diff --git a/results/sacred/5m_vs_6m/feudal/13/config.json b/results/sacred/5m_vs_6m/feudal/13/config.json
new file mode 100644
index 0000000..19628e1
--- /dev/null
+++ b/results/sacred/5m_vs_6m/feudal/13/config.json
@@ -0,0 +1,97 @@
+{
+ "action_selector": "epsilon_greedy",
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+ "enable_parallel_computing": false,
+ "env": "sc2",
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+ "debug": false,
+ "difficulty": "7",
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+ "heuristic_rest": false,
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+ "obs_timestep_number": false,
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+ "seed": null,
+ "state_last_action": true,
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+ "gamma": 0.99,
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+ "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
@@ -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": "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": "2024-12-29T19:10:51.714337",
+ "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": "2024-12-29T19:10:51.629933",
+ "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
+ },
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\ 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": {
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+ "mainfile": "main.py",
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+ "repositories": [
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+ "dirty": true,
+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
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+ {
+ "commit": "44bb188185fd0292a1a306c86396027efb44224d",
+ "dirty": true,
+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
+ }
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+ [
+ "main.py",
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+ "utils\\logging.py",
+ "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py"
+ ]
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+ "Traceback (most recent call last):\n",
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+ " 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"
+ ],
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+ "host": {
+ "ENV": {},
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\ 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,
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+ "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,
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+ "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,
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+}
\ 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": [
+ [
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+ ],
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+ "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"
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+ "env_args": {
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+}
\ 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 @@
+{
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\ 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 @@
+{
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+ 0.0
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+ "value": 0.0
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\ 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",
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+ "numpy==1.23.1",
+ "PyYAML==6.0.2",
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+ "mainfile": "main.py",
+ "name": "pymarl",
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+ "dirty": true,
+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
+ },
+ {
+ "commit": "44bb188185fd0292a1a306c86396027efb44224d",
+ "dirty": true,
+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
+ }
+ ],
+ "sources": [
+ [
+ "main.py",
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+ ],
+ [
+ "utils\\logging.py",
+ "_sources\\logging_f71df6d788e929fac28afdf951d63d54.py"
+ ]
+ ]
+ },
+ "fail_trace": [
+ "Traceback (most recent call last):\n",
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+ " 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",
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+ "total_memory": 16376
+ }
+ ]
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+ "Windows",
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+ "env_args": {
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+ "status": "FAILED",
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+}
\ 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,
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+ "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",
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+ "load_step": 0,
+ "local_results_path": "results",
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+ "lr": 0.0005,
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+ "mixer": "qmix",
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+ "optim_eps": 1e-05,
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+ "run": "default",
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+ "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"
+ },
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+ "command": "my_main",
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+ "batch_size_run": 4,
+ "buffer_size": 5000,
+ "env_args": {
+ "map_name": "5m_vs_6m"
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+ "obs_last_action": true,
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+ },
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+}
\ 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",
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+ "critic_lr": 0.0005,
+ "double_q": true,
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+ "enable_parallel_computing": false,
+ "env": "sc2",
+ "env_args": {
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+ "debug": false,
+ "difficulty": "7",
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+ "heuristic_ai": false,
+ "heuristic_rest": false,
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+ "obs_last_action": false,
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+ "obs_pathing_grid": false,
+ "obs_terrain_height": false,
+ "obs_timestep_number": false,
+ "replay_dir": "",
+ "replay_prefix": "",
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+ "reward_defeat": 0,
+ "reward_negative_scale": 0.5,
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+ "seed": null,
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+ },
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+ "epsilon_finish": 0.05,
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+ "label": "default_label",
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+ "load_step": 0,
+ "local_results_path": "results",
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+ "lr": 0.0005,
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+}
\ 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,
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+ "return_priority": false,
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\ 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"
+ }
+ ],
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+ [
+ "main.py",
+ "_sources\\main_888918ff84cf3bc1e6e9dcc8919870b2.py"
+ ],
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+ "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": {
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+ }
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+ "UPDATE": [
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\ 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 @@
+{
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+ "enable_parallel_computing": false,
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+ "debug": false,
+ "difficulty": "7",
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+ "heuristic_ai": false,
+ "heuristic_rest": false,
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+ "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
+ },
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+ "epsilon_finish": 0.05,
+ "epsilon_start": 1.0,
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+ "label": "default_label",
+ "learner": "nq_learner",
+ "learner_log_interval": 10000,
+ "load_step": 0,
+ "local_results_path": "results",
+ "log_interval": 10000,
+ "lr": 0.001,
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\ 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 @@
+{
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+ "command": "my_main",
+ "experiment": {
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+ }
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+ "start_time": "2024-12-29T19:15:59.846215",
+ "status": "INTERRUPTED",
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+}
\ 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 @@
+{
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\ 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
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new file mode 100644
index 0000000..9e26dfe
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\ No newline at end of file
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new file mode 100644
index 0000000..7d0308e
--- /dev/null
+++ b/results/sacred/5m_vs_6m/qmix/11/run.json
@@ -0,0 +1,104 @@
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+ "url": "https://github.com/tjuHaoXiaotian/pymarl3.git"
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\ 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 @@
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+ "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": [
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+ "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": [
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+ ],
+ "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": [
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+ ],
+ "return_min": [
+ {
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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
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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
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+{
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+ "command": "my_main",
+ "experiment": {
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+ " File \"C:\\Users\\Taiyo\\Desktop\\SMAC V2\\pymarl3\\src\\run\\run.py\", line 128, in run\n run_sequential(args=args, logger=logger)\n",
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+ "result": null,
+ "start_time": "2024-12-29T18:19:50.281614",
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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
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new file mode 100644
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+ "test_return_max": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.0
+ },
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 9.433962264150942
+ }
+ ],
+ "test_return_max_T": [
+ 91,
+ 10157
+ ],
+ "test_return_mean": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.0
+ },
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 6.662735849056603
+ }
+ ],
+ "test_return_mean_T": [
+ 91,
+ 10157
+ ],
+ "test_return_min": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.0
+ },
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 5.245283018867925
+ }
+ ],
+ "test_return_min_T": [
+ 91,
+ 10157
+ ],
+ "test_return_std": [
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.0
+ },
+ {
+ "dtype": "float64",
+ "py/object": "numpy.float64",
+ "value": 0.8664331680367857
+ }
+ ],
+ "test_return_std_T": [
+ 91,
+ 10157
+ ]
+}
\ No newline at end of file
diff --git a/results/sacred/5m_vs_6m/qmix/9/metrics.json b/results/sacred/5m_vs_6m/qmix/9/metrics.json
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/results/sacred/5m_vs_6m/qmix/9/metrics.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/results/sacred/5m_vs_6m/qmix/9/run.json b/results/sacred/5m_vs_6m/qmix/9/run.json
new file mode 100644
index 0000000..697d6f5
--- /dev/null
+++ b/results/sacred/5m_vs_6m/qmix/9/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": "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}
+ 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()
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diff --git a/src/__init__.py b/src/__init__.py
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diff --git a/src/components/__init__.py b/src/components/__init__.py
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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
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diff --git a/src/envs/smac_v1/official/maps/SMAC_Maps/so_many_baneling.SC2Map b/src/envs/smac_v1/official/maps/SMAC_Maps/so_many_baneling.SC2Map
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diff --git a/src/envs/smac_v1/official/maps/__init__.py b/src/envs/smac_v1/official/maps/__init__.py
new file mode 100644
index 0000000..4017bb3
--- /dev/null
+++ b/src/envs/smac_v1/official/maps/__init__.py
@@ -0,0 +1,10 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from . import 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
+)
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diff --git a/src/envs/smac_v2/official/maps/__init__.py b/src/envs/smac_v2/official/maps/__init__.py
new file mode 100644
index 0000000..4017bb3
--- /dev/null
+++ b/src/envs/smac_v2/official/maps/__init__.py
@@ -0,0 +1,10 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from . import 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