A simple chess environment for gym. It computes all available moves, including castling, pawn promotions and 3-fold repetition draws.

Install the module:

pip install -e .

There are 3 environments available: v0, v1 and v2. The original v0 version contains legacy code and is no longer supported, so it's recommended to use v1 or v2.

Both v1 and v2 share the same basic API so in most scenarios can be used interchangeably. The v1 version is implemented in pure Python, while v2 has its core logic implemented in Rust and is over 100 times faster. Hence, if performance and speed are of the essence, v2 is the way to go.

You can import the Python classes directly, or create pre-defined environments with gym:

import gym
from gym_chess import ChessEnvV1, ChessEnvV2

env1 = ChessEnvV1()
env2 = ChessEnvV2()

env1 = gym.make('ChessVsSelf-v1')
env2 = gym.make('ChessVsSelf-v2')

You can also play against a random bot:

env = gym.make('ChessVsSelf-v1')

Moves are pre-calculated for the current state and can be accessed from the environment. You can also access them in the form of actions from the environment action space.

Once you have chosen a move, make sure to convert it into an action (or select an action directly) and pass it to the environment to get the next state.

import random
from gym_chess import ChessEnvV1

env = ChessEnvV1() # or ChessEnvV2

# current state
state = env.state

# select a move and convert it into an action
moves = env.possible_moves
move = random.choice(moves)
action = env.move_to_actions(move)

# or select an action directly
actions = env.possible_actions
action = random.choice(actions)

# pass it to the env and get the next state
new_state, reward, done, info = env.step(action)

Reset the environment:

initial_state = env.reset()

Visualise the current state of the chess game:

env.render()

    -------------------------
 8 |  ♖  ♘  ♗  ♕  ♔  ♗  ♘  ♖ |
 7 |  ♙  ♙  ♙  ♙  ♙  ♙  ♙  ♙ |
 6 |  .  .  .  .  .  .  .  . |
 5 |  .  .  .  .  .  .  .  . |
 4 |  .  .  .  .  .  .  .  . |
 3 |  .  .  .  .  .  .  .  . |
 2 |  ♟  ♟  ♟  ♟  ♟  ♟  ♟  ♟ |
 1 |  ♜  ♞  ♝  ♛  ♚  ♝  ♞  ♜ |
    -------------------------
      a  b  c  d  e  f  g  h

You can also visualise multiple moves:

moves = env.possible_moves
env.render_moves(moves[10:12] + moves[16:18])

• opponent: can be "random", "none" or a function. Tells the environment whether to use a bot that picks a random move, play against self or use a specific bot policy (default: "random")
• log: True or False, specifies whether to log every move and render every new state (default: True)
• initial_state: initial board positions, the default value is the default chess starting board. You can specify a custom board. View scripts gym_chess/test/v1 for some examples
• player_color: "WHITE" or "BLACK", only useful if playing against a bot (default: "WHITE")

This method will calculate the possible moves. By default they are calculated at the current state for the current player (state.current_player).

• state: (optional) state for which to calculate the moves
• player: (optional) "WHITE" or "BLACK", specifies the player
• attack: if set to True, will return the attacks instead of moves

Moves are encoded as either:

• a tuple of coordinates ((from_x, from_y), (to_x, to_y))
• or a string e.g. "CASTLE_KING_SIDE_WHITE", "CASTLE_QUEEN_SIDE_BLACK", "RESIGN"

Moves are pre-calculated for every new state and stored in possible_moves.

v1 and v2 share most of the API, but the internals a little bit different.

For instance v1 stores the board matrix directly in the state as env.state, while in v2 the state is a dictionary where board can be accessed with env.state['board'].

>>> print(env.state) # v1
>>> print(env.state['board']) # v2

[[-3, -5, -4, -2, -1, -4, -5, -3],
 [-6, -6, -6, -6, -6, -6, -6, -6],
 [0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0, 0, 0],
 [6, 6, 6, 6, 6, 6, 6, 6],
 [3, 5, 4, 2, 1, 4, 5, 3]]

Every integer represents a piece. Positive pieces are white and negative ones are black.

Piece IDs are stored in constants that can be imported.

from gym_chess.envs.chess_v1 import (
    KING_ID,
    QUEEN_ID,
    ROOK_ID,
    BISHOP_ID,
    KNIGHT_ID,
    PAWN_ID,
)

The schema is:

EMPTY_SQUARE_ID = 0
KING_ID = 1
QUEEN_ID = 2
ROOK_ID = 3
BISHOP_ID = 4
KNIGHT_ID = 5
PAWN_ID = 6

Additional information can be found in other attributes of the environment:

env.current_player
env.white_king_castle_possible
env.white_queen_castle_possible
env.black_king_castle_possible
env.black_queen_castle_possible
env.white_king_on_the_board
env.black_king_on_the_board

Examples can be found in gym_chess/example. The v1 examples are valid for both the v1 and v2 environments.

Run all the tests with pytest.

Code fixing is done with black with max line width of 100 characters with the command black -l 100 . No config needed.

Rust code is formatted with cargo fmt.

The v2 environment uses a chess engine implemented in Rust that uses PyO3 to bind to the Python interpreter. Rust is an amazing compiled language and this project holds 2 configurations:

• Cargo.py.toml is used to build the library into a Python module with setup.py
• Cargo.dev.toml is used to build directly with cargo and to access the library in the main.rs script for development purposes

Note: we haven't found a way to specify the Cargo toml file to either process, so copy the contents of the config you want to use into Cargo.toml to make it work.

En-passant moves are not currently supported in the V1 environment.

• https://pyo3.rs/v0.4.1/print.html
• https://github.com/PyO3/pyo3
• https://www.forrestthewoods.com/blog/how-to-debug-rust-with-visual-studio-code/

The v2 environment is over 100 times faster than the v1 environment. However, since most of the code is written in Rust, it's generally harder to debug.

from gym_chess import ChessEnvV1, ChessEnvV2

env_v1 = ChessEnvV1()
env_v2 = ChessEnvV2()

# v1: written in Python
>>> %timeit -n 50 -r 8 env_v1.get_possible_moves()
## 29.5 ms ± 872 µs per loop (mean ± std. dev. of 8 runs, 50 loops each)

# v2: compiled in Rust
>>> %timeit -n 50 -r 8 env_v2.get_possible_moves()
## 240 µs ± 31.9 µs per loop (mean ± std. dev. of 8 runs, 50 loops each)