3d-connectX-env 1.0.0

3D ConnectX for OpenAI Gym.

3d-connect4-gym

3D connectX repository, developed for the OpenAI Gym format.

Installation

The preferred installation of 3d-connectX-env is from pip:

pip install 3d-connectX-env

Usage

Python

from gym_3d_connectX.envs import AnyNumberInARow3dEnv

env = AnyNumberInARow3dEnv()
env.reset()

env.utils.win_reward = 100
env.utils.draw_penalty = 50
env.utils.lose_penalty = 100
env.utils.could_locate_reward = 10
env.utils.couldnt_locate_penalty = 10
env.utils.time_penalty = 1
env.player = 1
actions = [0, 0, 1, 1, 2, 2, 4, 4, 0, 0, 1, 1, 2, 2, 0, 3]

for action in actions:
    obs, reward, done, info = env.step(action)
    env.render(mode="plot")

Environments

The environments only send reward-able game-play frames to agents; No cut-scenes, loading screens, etc. are sent to an agent nor can an agent perform actions during these instances.

Environment: 3d-connectX-v0

Factor at initialization.

Key	Type	Description
num_grid	int	Length of a side.
num_win_seq	int	The number of sequence necessary for winning.
win_reward	float	The reward agent gets when win the game.
draw_penalty	float	The penalty agent gets when it draw the game.
lose_penalty	float	The penalty agent gets when it lose the game.
couldnt_locate_penalty	float	The penalty agent gets when it choose the location where the stone cannot be placed.
could_locate_reward	float	The additional reward for agent being able to put the stone.
time_penalty	float	The penalty agents gets along with timesteps.
first_player	int	Define which is the first player.

Step

Info about the rewards and info returned by the step method.

Key	Type	Description
turn	int	The number of the player at this step
winner	int	Value of the player on the winning side
is_couldnt_locate	bool	In this step the player chooses where to place the stone.