2-player Connect4 gym environment

OpenAI Gym environment for the classic board game - Connect4. Designed for competitive reinforcement learning, requires two agents to play. Designed for speed. Pure python, but uses bitboards (bitwise operations) to try and get you the maximum simulation speed.

Requirements

• Python 3

Installation

Use pip to install the git repo

pip install -e git+ssh://[email protected]/davidcotton/gym-connect4@master#egg=gym-connect4

Usage

Import Gym and the Connect4 environment

import gym
import gym_connect4

Build a new (default) Connect4 environment via the usual Gym factory method

env = gym.make('Connect4Env-v0')

Then use similar to usual Gym workflow run the env, except that both players receive obs and generate an action each turn.

agents = ['Agent1()', 'Agent2()']
obses = env.reset()  # dict: {0: obs_player_1, 1: obs_player_2}
game_over = False
while not game_over:
    action_dict = {}
    for agent_id, agent in enumerate(agents):
        action = env.action_space.sample()
        action_dict[agent_id] = action
    
    obses, rewards, game_over, info = env.step(action_dict)
    env.render()

As Connect4 is an alternating turn game, the env is structured so both agents receive an obs at every time step, even on their opponents turn. However, during an opponents turn, the only legal move for an agent is a special "pass" action. The "pass" action is encoded to be the last action in both the action space and the action mask.

The action space is each column, [0-6] (for default width 7 Connect4) plus an extra "pass" action. e.g.

gym.spaces.Discrete(8)

The observation space is a dictionary containing: the action mask and the game board, e.g.

gym.spaces.Dict({
    'action_mask': gym.spaces.Box(low=0, high=1, shape=(self.game.board_width + 1,), dtype=np.uint8),
    'board': gym.spaces.Box(low=0, high=2, shape=(self.game.board_height, self.game.board_width), dtype=np.uint8),
})

These can be accessed like a normal Python dict, e.g.

obs, reward, game_over, info = env.step(action_dict)
action_mask = obs['action_mask']

>>> numpy.ndarray([1, 1, 1, 0, 1, 1, 1, 0])

Configuration

Custom Game Initialisation

Optionally, you can change the default environment configuration

env_config = {
    'board_height': 4,
    'board_width': 5,
}
env = gym.make('Connect4Env-v0', env_config=env_config)

The available parameters are:

Param	Description	Default
board_height	The number of rows on the connect4 board	6
board_width	The number of columns on the connect4 board	7
win_length	The number of consecutive discs need to win	4
reward_win	The utility of winning	1.0
reward_lose	The utility of losing	-1.0
reward_draw	The utility of a draw	0.0
reward_step	The utility of each turn	0.0

Custom Game States

If you are using a search-based algorithm such as MCTS or Minimax, you might want the ability to initialise the a game in a custom state, e.g. part way through a game. Rather than building the full Gym environment, you can just use the inner game class with a custom config and/or game state:

from gym_connect4.envs.connect4_env import Connect4

board = np.array([0, 0, 0, ...])
game = Connect4(game_state={'board': board, 'player': 1})
game.move(3)

Credits

Based on BielStela's Connect4 game, I just made it into a Gym environment.