- This is an ALPHA release

Deep MARL framework

Includes implementations of algorithms:

COMA
IQL
VDN
QMIX

Installation instructions

Build the Dockerfile using

cd docker
bash build.sh

Set up StarCraft I

bash install_sc1.sh

This will download the necessary sc1 files from this repo into the 3rdparty folder.

Set up StarCraft II.

bash install_sc2.sh

This will download SC2 into the 3rd party 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=2s_3z

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 stuff using the Docker container:

bash run.sh $GPU python3 src/main.py --config=qmix --env-config=sc2 with env_args.map_name=2s_3z

All results will be stored in the Results folder.

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 StarCraftII 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 StarCraftII. 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

(The window size is quite small at the moment, but will be fixed once deepmind accepts my pull request).

Potential Issues

StarCraft1 env is untested and might not behave as expected

Documentation/Support

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

License

Code licensed under the Apache License v2.0

bluecontra/pymarl_alpha