- Dec 4th - Updated to use SMAC V1. 

Python MARL framework

PyMARL is WhiRL's framework for deep multi-agent reinforcement learning and includes implementations of the following algorithms:

• QMIX: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• COMA: Counterfactual Multi-Agent Policy Gradients
• VDN: Value-Decomposition Networks For Cooperative Multi-Agent Learning
• IQL: Independent Q-Learning
• QTRAN: QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

PyMARL is written in PyTorch and uses SMAC as its environment.

Installation instructions

Build the Dockerfile using

cd docker
bash build.sh

Set up StarCraft II and SMAC:

bash install_sc2.sh

This will download SC2 into the 3rdparty folder and copy the maps necessary to run over.

The requirements.txt file can be used to install the necessary packages into a virtual environment (not recomended).

Run an experiment

python3 src/main.py --config=qmix --env-config=sc2 with env_args.map_name=2s3z

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

To run experiments using the Docker container:

bash run.sh $GPU python3 src/main.py --config=qmix --env-config=sc2 with env_args.map_name=2s3z

All results will be stored in the Results folder.

The previous config files used for the SMAC Beta have the suffix _beta.

Saving and loading learnt models

Saving models

You can save the learnt models to disk by setting save_model = True, which is set to False by default. The frequency of saving models can be adjusted using save_model_interval configuration. Models will be saved in the result directory, under the folder called models. The directory corresponding each run will contain models saved throughout the experiment, each within a folder corresponding to the number of timesteps passed since starting the learning process.

Loading models

Learnt models can be loaded using the checkpoint_path parameter, after which the learning will proceed from the corresponding timestep.

Watching StarCraft II replays

save_replay option allows saving replays of models which are loaded using checkpoint_path. Once the model is successfully loaded, test_nepisode number of episodes are run on the test mode and a .SC2Replay file is saved in the Replay directory of StarCraft II. Please make sure to use the episode runner if you wish to save a replay, i.e., runner=episode. The name of the saved replay file starts with the given env_args.save_replay_prefix (map_name if empty), followed by the current timestamp.

The saved replays can be watched by double-clicking on them or using the following command:

python -m pysc2.bin.play --norender --rgb_minimap_size 0 --replay NAME.SC2Replay

Note: Replays cannot be watched using the Linux version of StarCraft II. Please use either the Mac or Windows version of the StarCraft II client.

Documentation/Support

Documentation is a little sparse at the moment (but will improve!). Please raise an issue in this repo, or email Tabish

Help

To see available comand options, specify both --config and --env-config e.g.:

python src/main.py --config=qmix_smac --env-config=sc2 --help

Running without CUDA

PyMARL will use CUDA if a GPU is available. In some cases, a GPU is available but it does't meet the requirements for training a model. To force running in CPU mode, set the use_cuda option eg.:

python src/main.py --config=qmix_smac --env-config=sc2 with env_args.map_name=2s3z use_cuda=False

Citing PyMARL

If you use PyMARL in your research, please cite the SMAC paper.

M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T.G.J. Rudner, C.-M. Hung, P.H.S. Torr, J. Foerster, S. Whiteson. The StarCraft Multi-Agent Challenge, CoRR abs/1902.04043, 2019.

In BibTeX format:

@article{samvelyan19smac,
  title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},
  author = {Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon Whiteson},
  journal = {CoRR},
  volume = {abs/1902.04043},
  year = {2019},
}

License

Code licensed under the Apache License v2.0