gym-tic-tac-toe3D 0.0.2

Gym Environment for 3D Tic Tac Toe

gym-tic-tac-toe3D

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OpenAI Gym Environment for a Two-Player 3D Tic-Tac-Toe. Github Link: https://github.com/OUStudent/gym_tic_tac_toe3D

Requirements

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• gym
• Numpy
• Matplotlib

Install

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pip install gym-tic-tac-toe3D

How it Works

Tic Tac Toe is usually played on a 3x3 grid where the objective is for one player to line up their tokens in a straight line of three. This is an extremely easy and trivial game; however, one can extend the difficulty by stacking 3x3 layers to create a 3x3x3 cube. Now the objective is to line up three tokens in any of the directions.

Here are three example games where the action was randomly generated:

How to Use

The environment is a Two-Player Game, where Blue denotes Player 1 and Red denotes Player 2. The state and action contains all 27 possible positions, 9 for the first layer, 9 for the second, and 9 for the third.

The input has three possible integer values for eac position, -1 for opponent, 0 for empty, and 1 for current player. Note that no matter whom the Player is, these values holds true. The reward is a two value list where the first index represents the reward for Player 1 and the second for Player 2. The reward value should only be used after the game is completed. Players are rewarded for wins and receive extra points for how fast they win; while players are penalized for losing and how fast they lost. For example, after a game has completed the final reward could be [20, -10], which rewards Player 1 20 points while penalizing Player 2 -10 points.

Here is an example on how to create the environment:

import gym
import gym_tic_tac_toe3D
env = gym.make("tic_tac_toe3D-v0")

games = 3  # best of three
player1_reward = 0
player2_reward = 0
for i in range(0, games):
    state = env.reset()
    done = False
    player = 1
    while not done:
        env.render(player=player)
        plt.pause(0.5)
        while True:
            action = env.action_space.sample()
            # Need to check if action is available in state space
            if state[action] == 0:  
                break
    
        state, reward, done, info = env.step(action, player=player)
        # switch players
        if player == 1:
            player = 2
        else:
            player = 1
    # final render after completion of game to see final move
    env.render(player=player)
    plt.pause(1)
    player1_reward += reward[0]
    player2_reward += reward[1]