https://github.com/qlan3/gym-games
A collection of Gymnasium compatible games for reinforcement learning.
https://github.com/qlan3/gym-games
atari deep-learning deep-reinforcement-learning exploration-game games gym-environments gym-pygame gymnasium minatar pygame reinforcement-learning
Last synced: 21 days ago
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A collection of Gymnasium compatible games for reinforcement learning.
- Host: GitHub
- URL: https://github.com/qlan3/gym-games
- Owner: qlan3
- License: mit
- Created: 2019-06-03T17:01:17.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2025-06-19T04:15:51.000Z (8 months ago)
- Last Synced: 2025-09-22T22:58:19.802Z (4 months ago)
- Topics: atari, deep-learning, deep-reinforcement-learning, exploration-game, games, gym-environments, gym-pygame, gymnasium, minatar, pygame, reinforcement-learning
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 84
- Watchers: 3
- Forks: 23
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Gym Games
This is a collection of Gymnasium compatible games for reinforcement learning.
> [!NOTE]
> For Gym compatible version, please check [v1.0.4](https://github.com/qlan3/gym-games/releases/tag/v1.0.4).
For [PyGame Learning Environment](https://pygame-learning-environment.readthedocs.io/en/latest/user/games.html), the default observation is a non-visual state representation of the game.
For [MinAtar](https://github.com/kenjyoung/MinAtar), the default observation is a visual input of the game.
## Environments
- PyGame learning environment:
- Catcher-PLE-v0
- FlappyBird-PLE-v0
- Pixelcopter-PLE-v0
- PuckWorld-PLE-v0
- Pong-PLE-v0
- MinAtar:
- Asterix-MinAtar-v1
- Breakout-MinAtar-v1
- Freeway-MinAtar-v1
- Seaquest-MinAtar-v1
- SpaceInvaders-MinAtar-v1
- Exploration games:
- NChain-v1
- LockBernoulli-v0
- LockGaussian-v0
- SparseMountainCar-v0
- DiabolicalCombLock-v0
## Installation
### Gymnasium
Please read the instruction [here](https://github.com/Farama-Foundation/Gymnasium).
### Pygame
- On OSX: See [here](https://www.pygame.org/wiki/MacCompile) for more details.
brew install sdl2 sdl2_image sdl2_mixer sdl2_ttf pkg-config
pip install pygame==2.5.2
- On Ubuntu:
sudo apt-get -y install python-pygame
pip install pygame==2.5.2
- Others: Please read the instruction [here](http://www.pygame.org/wiki/GettingStarted#Pygame%20Installation).
### PyGame Learning Environment
git clone https://github.com/ntasfi/PyGame-Learning-Environment.git
cd PyGame-Learning-Environment/
pip install -e .
## MinAtar
pip install minatar==1.0.15
### Gym-games
pip install git+https://github.com/qlan3/gym-games.git
## Example
Run ``python test.py``.
## Cite
Please use this bibtex to cite this repo:
```
@misc{gym-games,
author = {Lan, Qingfeng},
title = {Gym Compatible Games for Reinforcement Learning},
year = {2019},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/qlan3/gym-games}}
}
```
## References
- [gym](https://github.com/openai/gym/)
- [gym-ple](https://github.com/lusob/gym-ple)
- [SRNN](https://github.com/VincentLiu3/SRNN)
- [MinAtar](https://github.com/kenjyoung/MinAtar)
- [Latent State Decoding](https://github.com/microsoft/StateDecoding)
- [Gymnasium](https://github.com/Farama-Foundation/Gymnasium)