tictactoe-gym 1.0.1

An OpenAI gym environment for playing Tic Tac Toe

tictactoe-gym

This is an OpenAI gym environment for playing Tic Tac Toe (or Noughts and Crosses).

Installation

pip install tictactoe_gym

Overview

Each player takes it in turn to mark a position in a square grid (e.g., 3x3), until they form a horizontal, vertical or diagonal line across the grid (e.g., 3 in a row), in which case they win. If no further moves can be made and there is no winner then the game is a draw.

Action Space

The action is an integer which can take values ${0, 1, ..., n^{2} - 1}$, where $n$ is the size of the grid, starting at grid position $[0, 0]$, which is action $0$, then on to $[0, 1]$ which is action $1$, and moving through row by row, until action $n^{2} - 1$, which is $[n - 1, n - 1]$.

For example, action 4 is the centre position, $[1, 1]$, in a $3 \times 3$ game.

Observation Space

The observation is a ndarray with shape (n, n), where n is the grid size. Each entry can take the following values:

Value	Meaning
0	No mark here yet, free to mark
1	Player 1 has placed a mark here
-1	Player 2 has placed a mark here

Rewards

A reward of +1 is given to the winning player with a reward of -1 for the losing player. If it's a draw both players get a reward of 0.

Starting State

$n \times n$ grid of zeros.

Episode End

The episode ends if any one of the following occurs:

1. Termination: A player gets $n$ successive marks in a row, column or diagonal for an $n \times n$ grid.
2. Termination: No more moves can be played (i.e., every grid position is marked).

Arguments

gym.make('tictactoe-v0')

No additional arguments are currently supported.

Environment

Attributes

size (int): The size of the grid.

observation_space (gym.spaces.Box): The observation space, an $n \times n$ grid.

action_space (gym.spaces.Discrete): The action space, an $n^{2} - 1$ list.

reward_range (int): The reward range, -1 for a loss, 0 for a draw, +1 for a win.

_player (int): The current player, 1/-1.

_state_size (tuple): A (1, 3) tuple of the state size - (size, size, 1).

_action_to_index_map (dict): A mapping from actions to indices, e.g., {0: [0, 0], 1: [0, 1], 2: [0, 2], 3: [1, 0], ..., 8: [2, 2]}.

_history (list) : The history of actions, e.g., [0, 5, 1, 3, 2].

_terminal (bool): Whether the game is finished or not.

_winner (int): The winning player, 1 for player 1, -1 for player 2 and 0 for a draw.

Methods

init: Initialises environment and all attributes.

get_observation: Gets the observation for a player.

get_actions: Get possible actions, i.e., positions where marks have not yet been made.

get_result: Get result for a player. If the player is 1/-1 and winner is 1/-1, then return 1, as the requested player has won. Otherwise if the player is 1/-1 and winner is -1/1, then return -1, as the player has lost. If a draw, then self._winner is 0, so returns 0.

_get_action_to_index_map: Returns the action_to_index_map. This is more efficient as it saves having to compute indices every time.

_is_valid_action: Checks an action is valid.

_row_winner: Returns winner of any row from an observation.

_col_winner: Returns winner of any column from an observation.

_main_diag_winner: Returns winner of main diagonal from an observation.

_reverse_main_diag_winner: Returns winner of reverse main diagonal from an observation.

_is_game_over: Returns true if game is over and false otherwise. Also sets the winning player as the _winner or 0 for draw.

_get_info: Get information on the game.

step: Step game using action and returns new observation, winner, game over indicator, truncated (always False here) and info.

reset: Reset the game.

clone: Clone the game.

render: Renders the current observation in the terminal as a string.