Gym Games
This is a gym compatible version of various games for reinforcenment learning.
For PyGame Learning Environment, the default observation is a non-visual state representation of the game.
For 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-v0
- Breakout-MinAtar-v0
- Freeway-MinAtar-v0
- Seaquest-MinAtar-v0
- Space_invaders-MinAtar-v0
Installation
Gym
Please read the instruction here.
Pygame
-
On OSX:
brew install sdl sdl_ttf sdl_image sdl_mixer portmidi pip install pygame -
On Ubuntu:
sudo apt-get -y install python-pygame pip install pygame -
Others: Please read the instruction here.
PyGame Learning Environment
pip install git+https://github.com/ntasfi/PyGame-Learning-Environment.git
MinAtar
pip install git+https://github.com/kenjyoung/MinAtar.git
Gym-games
-
Install from source:
pip install git+https://github.com/qlan3/gym-games.git -
Install from PyPi:
pip install gym-games
Example
Run python test.py.
Cite
Please use this bibtex to cite this repo:
@misc{gym-games,
author = {Qingfeng, Lan},
title = {Gym Compatible Games for Reinforcenment Learning},
year = {2019},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/qlan3/gym-games}}
}
