Metadata-Version: 1.2
Name: gym-games
Version: 1.0.3
Summary: This is a gym version of various games for reinforcenment learning.
Home-page: https://github.com/qlan3/gym-games
Author: qlan3
Author-email: qlan3@ualberta.ca
License: MIT
Description: # 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)
Keywords: AI,Reinforcement Learning,Games,Pygame,MinAtar
Platform: UNKNOWN
Requires-Python: >=3.5
