- This is an ALPHA releaseIncludes implementations of algorithms:
- COMA
- IQL
- VDN
- QMIX
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).
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.
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.
Learnt models can be loaded using the checkpoint_path parameter, after which the learning will proceed from the corresponding timestep.
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).
- StarCraft1 env is untested and might not behave as expected
Documentation is a little sparse at the moment (but will improve!). Please raise an issue in this repo, or email Tabish
Code licensed under the Apache License v2.0