boardgame2
boardgame2 is an extension of OpenAI Gym that implements multiple two-player zero-sum 2-dimension board games, such as TicTacToe, Gomuko, and Reversi.
Environments
Reversi-v0-
KInARow-v0, as well asGomuku-v0andTicTacToe-v0 -
Go-v0(Experimental, not fully implemented)
Install
pip install --upgrade boardgame2
We support Windows, macOS, Linux, and other operating systems.
Usage
See API docs for all classes and functions.
Create a Game
import gym
import boardgame2
env = gym.make('TicTacToe-v0') # 3x3, 3-in-a-row
env = gym.make('Gomuku-v0') # 15x15, 5-in-a-row
env = gym.make('KInARow-v0', board_shape=5, target_length=4) # 5x5, 4-in-a-row
env = gym.make('KInARow-v0', board_shape=(3, 5), target_length=4) # 3x5, 4-in-a-row
env = gym.make('Reversi-v0') # 8x8
env = gym.make('Reversi-v0', board_shape=6) # 6x6
env = gym.make('Go-v0') # 19x19
env = gym.make('Go-v0', board_shape=15) # 15x15
Play a Game
import gym
import boardgame2
env = gym.make('TicTacToe-v0')
print('observation space = {}'.format(env.observation_space))
print('action space = {}'.format(env.action_space))
observation, info = env.reset()
while True:
action = env.action_space.sample()
observation, reward, termination, truncation, info = env.step(action)
if termination or truncation:
break
env.close()
BibTeX
This package has been published in the following book:
@book{xiao2019,
title = {Reinforcement Learning: Theory and {Python} Implementation},
author = {Zhiqing Xiao}
year = 2019,
month = 8,
publisher = {China Machine Press},
}
