gym-games's Introduction

Gym Games

This is a gym compatible version of various games for reinforcement 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
- Asterix-MinAtar-v1
- Breakout-MinAtar-v0
- Breakout-MinAtar-v1
- Freeway-MinAtar-v0
- Freeway-MinAtar-v1
- Seaquest-MinAtar-v0
- Seaquest-MinAtar-v1
- SpaceInvaders-MinAtar-v0
- SpaceInvaders-MinAtar-v1
Exploration games:
- NChain-v1
- LockBernoulli-v0
- LockGaussian-v0
- SparseMountainCar-v0
- DiabolicalCombLock-v0

Installation

Gym

Please read the instruction here.

Pygame

On OSX:

brew install sdl sdl_ttf sdl_image sdl_mixer portmidi
pip install pygame==1.9.6

On Ubuntu:

sudo apt-get -y install python-pygame
pip install pygame==1.9.6

Others: Please read the instruction here.

PyGame Learning Environment

pip install git+https://github.com/ntasfi/PyGame-Learning-Environment.git

MinAtar

pip install minatar==1.0.10

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}}
}