gym-games 1.0.3

# Gym Games

This is a gym compatible version of various games for reinforcenment learning.

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-v0 - Breakout-MinAtar-v0 - Freeway-MinAtar-v0 - Seaquest-MinAtar-v0 - Space_invaders-MinAtar-v0

## Installation

### Gym

Please read the instruction [here](https://github.com/openai/gym).

### 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](http://www.pygame.org/wiki/GettingStarted#Pygame%20Installation).

### 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}} }

## References

• [gym](https://github.com/openai/gym/tree/master/)

• [gym-ple](https://github.com/lusob/gym-ple)

• [SRNN](https://github.com/VincentLiu3/SRNN)

• [MinAtar](https://github.com/kenjyoung/MinAtar)