Chess Gym

Gym Chess is an environment for reinforcement learning with the OpenAI gym module.

Installation

1. Install OpenAI Gym and its dependencies.
   pip install gym

2. Download and install chess_gym:
   git clone https://github.com/Ryan-Rudes/chess-gym.git
cd chess-gym
python setup.py install
   
   Or, you can use pip (you may view the package here):
   pip install chess-gym

Environments

Example

You can use the standard Chess-v0 environment as so:

import gym
import chess_gym

env = gym.make("Chess-v0")
env.reset()

terminal = False

while not terminal:
  action = env.action_space.sample()
  observation, reward, terminal, info = env.step(action)
  env.render()
  
env.close()

There is also an environment for the Chess960 variant; its identifier is Chess960-v0

Further Info

This environment will return 0 reward until the game has reached a terminal state. In the case of a draw, it will still return 0 reward. Otherwise, the reward will be either 1 or -1, depending upon the winning player.

observation, reward, terminal, info = env.step(action)

Here, info will be a dictionary containing the following information pertaining to the board configuration and game state:

• turn: The side to move (chess.WHITE or chess.BLACK).
• castling_rights: Bitmask of the rooks with castling rights.
• fullmove_number: Counts move pairs. Starts at 1 and is incremented after every move of the black side.
• halfmove_clock: The number of half-moves since the last capture or pawn move.
• promoted: A bitmask of pieces that have been promoted.
• ep_square: The potential en passant square on the third or sixth rank or None.