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https://github.com/oxwhirl/pymarl
Python Multi-Agent Reinforcement Learning framework
https://github.com/oxwhirl/pymarl
Last synced: 5 days ago
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Python Multi-Agent Reinforcement Learning framework
- Host: GitHub
- URL: https://github.com/oxwhirl/pymarl
- Owner: oxwhirl
- License: apache-2.0
- Created: 2018-10-25T13:48:43.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:58:39.000Z (about 2 years ago)
- Last Synced: 2024-11-29T06:28:49.669Z (13 days ago)
- Language: Python
- Homepage:
- Size: 276 KB
- Stars: 1,890
- Watchers: 30
- Forks: 387
- Open Issues: 60
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-real-world-rl - Python MARL
README
```diff
- Please pay attention to the version of SC2 you are using for your experiments.
- Performance is *not* always comparable between versions.
- The results in SMAC (https://arxiv.org/abs/1902.04043) use SC2.4.6.2.69232 not SC2.4.10.
```
# Python MARL framework
PyMARL is [WhiRL](http://whirl.cs.ox.ac.uk)'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](https://arxiv.org/abs/1803.11485)
- [**COMA**: Counterfactual Multi-Agent Policy Gradients](https://arxiv.org/abs/1705.08926)
- [**VDN**: Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://arxiv.org/abs/1706.05296)
- [**IQL**: Independent Q-Learning](https://arxiv.org/abs/1511.08779)
- [**QTRAN**: QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://arxiv.org/abs/1905.05408)
PyMARL is written in PyTorch and uses [SMAC](https://github.com/oxwhirl/smac) as its environment.
## Installation instructions
Build the Dockerfile using
```shell
cd docker
bash build.sh
```
Set up StarCraft II and SMAC:
```shell
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
```shell
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:
```shell
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:
```shell
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](mailto:[email protected])
## Citing PyMARL
If you use PyMARL in your research, please cite the [SMAC paper](https://arxiv.org/abs/1902.04043).
*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:
```tex
@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