etnguyen03 / gym_reversi

openAI gym env for reversi/othello game

OpenAI Gym Reversi/Othello

This is original "Reversi env" for reinforcement learning.

Usage

cd ${GYM_REVERSI_PATH}
pip install -e .

Script

import reversi.envs.reversi
import gym
import random
import numpy as np
env = gym.make('Reversi8x8-v0')
env.reset()
for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
        enables = env.possible_actions
        # if nothing to do ,select pass
        if len(enables)==0:
            action = env.board_size**2 + 1
        # random select (update learning method here)
        else:
            action = random.choice(enables)
        observation, reward, done, info = env.step(action)
        env.render()
        if done:
            print("Episode finished after {} timesteps".format(t+1))
            black_score = len(np.where(env.state[0,:,:]==1)[0])
            print(black_score)
            break

      1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |
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1  |  B  |  B  |  B  |  B  |  B  |  B  |  B  |  B  |
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2  |  B  |  W  |  W  |  B  |  B  |  W  |  W  |  B  |
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3  |  B  |  W  |  W  |  W  |  B  |  B  |  W  |  B  |
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4  |  B  |  W  |  B  |  W  |  W  |  B  |  B  |  B  |
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5  |  B  |  W  |  W  |  B  |  W  |  W  |  B  |  B  |
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6  |  B  |  W  |  B  |  W  |  W  |  B  |  W  |  B  |
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7  |  B  |  W  |  B  |  B  |  B  |  B  |  B  |  B  |
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8  |  B  |  W  |  W  |  W  |  W  |  W  |  W  |  B  |
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Original Source: https://github.com/pigooosuke/gym_reversi