About

An environment for the board game Go. It is implemented using OpenAI's Gym API.

Installation

# In the root directory
pip install -e .

Example

# In the root directory
python3 demo.py

Code Example

import gym

go_env = gym.make('gym_go:go-v0', size=7, reward_method='real')

first_action = (2,5)
second_action = (5,2)
state, reward, done, info = go_env.step(first_action)
go_env.render('terminal')

    0   1   2   3   4   5   6
  -----------------------------
0 |   |   |   |   |   |   |   |
  -----------------------------
1 |   |   |   |   |   |   |   |
  -----------------------------
2 |   |   |   |   |   | B |   |
  -----------------------------
3 |   |   |   |   |   |   |   |
  -----------------------------
4 |   |   |   |   |   |   |   |
  -----------------------------
5 |   |   |   |   |   |   |   |
  -----------------------------
6 |   |   |   |   |   |   |   |
  -----------------------------
	Turn: WHITE, Last Turn Passed: False, Game Over: False
	Black Area: 49, White Area: 0, Reward: 0

state, reward, done, info = go_env.step(second_action)
go_env.render('terminal')

    0   1   2   3   4   5   6
  -----------------------------
0 |   |   |   |   |   |   |   |
  -----------------------------
1 |   |   |   |   |   |   |   |
  -----------------------------
2 |   |   |   |   |   | B |   |
  -----------------------------
3 |   |   |   |   |   |   |   |
  -----------------------------
4 |   |   |   |   |   |   |   |
  -----------------------------
5 |   |   | W |   |   |   |   |
  -----------------------------
6 |   |   |   |   |   |   |   |
  -----------------------------
	Turn: BLACK, Last Turn Passed: False, Game Over: False
	Black Area: 1, White Area: 1, Reward: 0

Scoring

We use simple area scoring to determine the winner. A player's area is defined as the number of empty points a player's pieces surround plus the number of player's pieces on the board. The winner is the player with the larger area (a game is tied if both players have an equal amount of area on the board).

Game Ending

A game ends when both players pass consecutively

Reward Methods

Reward methods are in black's perspective

Real:
- If game ended:
  - -1 - White won
  - 0 - Game is tied
  - 1 - Black won
- 0 - Otherwise
Heuristic: If the game is ongoing, the reward is black area - white area. If black won, the reward is BOARD_SIZE**2. If white won, the reward is -BOARD_SIZE**2. If tied, the reward is 0.

State

The state object that is returned by the reset and step functions of the environment is a 6 x BOARD_SIZE x BOARD_SIZE numpy array. All values in the array are either 0 or 1

First and second channel: represent the black and white pieces respectively.
Third channel: Indicator layer for whose turn it is
Fourth channel: Invalid moves (including ko-protection) for the next action
Fifth channel: Indicator layer for whether the previous move was a pass
Sixth channel: Indicator layer for whether the game is over

Action

The step function takes in the action to execute and can be in the following forms:

a tuple/list of 2 integers representing the row and column or None for passing
a single integer representing the action in 1d space (i.e 9 would be (1,2) in and 49 would be a pass for a 7x7 board)

mattrym/GymGo