https://github.com/michaelfeil/skyjo_rl
Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
https://github.com/michaelfeil/skyjo_rl
mutli-agent pettingzoo reinforcement-learning rllib
Last synced: 21 days ago
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Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
- Host: GitHub
- URL: https://github.com/michaelfeil/skyjo_rl
- Owner: michaelfeil
- License: mit
- Created: 2022-01-26T17:02:59.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2025-03-06T22:34:42.000Z (3 months ago)
- Last Synced: 2025-04-26T02:30:26.232Z (26 days ago)
- Topics: mutli-agent, pettingzoo, reinforcement-learning, rllib
- Language: Jupyter Notebook
- Homepage: https://michaelfeil.eu/skyjo_rl/
- Size: 3.64 MB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
skyjo_rl
==============================Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
[](https://codecov.io/gh/michaelfeil/skyjo_rl)
[Read the docs](https://michaelfeil.github.io/skyjo_rl/)
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------------
[Github Repository](https://github.com/michaelfeil/skyjo_rl)├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
│
├── docs <- Docs HTMLs, see Sphinx [docs](https:/michaelfeil.github.io/skyjo_rl)
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
├── requirements.txt <- requirements for the rlskyjo
├── requirements_dev.txt <- requirements for developers
├── rlskyjo
│ ├── environment
│ │ ├── skyjo_env.py
│ │ └── vanilla_env_example.py
│ ├── game
│ │ ├── sample_game.py
│ │ └── skyjo.py
│ ├── models
│ │ ├── action_mask_model.py
│ │ ├── random_admissible_policy.py
│ │ └── train_model_simple_rllib.py
│ └── utils.py
├── setup.py <- makes project pip installable (pip install -e .) so skyjo_rl can be imported
├── test_environment.py
├── tests <- Unittests
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io--------
## PYPI Install
```
conda create --name skyjo python=3.8 pip
conda activate skyjo
pip install rlskyjo
```
--------
## Developer Install
```
git clone https://github.com/michaelfeil/skyjo_rl.git
conda create --name skyjo python=3.8 pip
conda activate skyjo
pip install -r requirements.txt
pip install -r requirements_dev.txt
pip install -e .
pre-commit install
coverage run -m --source=./rlskyjo pytest tests
```## Tutorials
[Vanilla SkyJo PettingZoo Env example](https://github.com/michaelfeil/skyjo_rl/blob/master/rlskyjo/environment/vanilla_env_example.py)[SkyJo game example](https://github.com/michaelfeil/skyjo_rl/blob/master/rlskyjo/game/sample_game.py)
[Train PPO MultiAgent with SkyJo PettingZoo Env, Pytorch and RLLib](https://github.com/michaelfeil/skyjo_rl/blob/master/rlskyjo/models/train_model_simple_rllib.py)
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[linkedin-url]: https://linkedin.com/in/michael-feil