Environments

coax.envs.ConnectFourEnv

An adversarial environment for playing the Connect-Four game.

This is a collection of environments currently not included in Gymnasium.

Object Reference

class coax.envs.ConnectFourEnv[source]

An adversarial environment for playing the Connect-Four game.

Variables:

  • action_space (gymnasium.spaces.Discrete(7)) – The action space.

  • observation_space (MultiDiscrete(nvec)) –

    The state observation space, representing the position of the current player’s tokens (s[1:,:,0]) and the other player’s tokens (s[1:,:,1]) as well as a mask over the space of actions, indicating which actions are available to the current player (s[0,:,0]) or the other player (s[0,:,1]).

    Note: The “current” player is relative to whose turn it is, which means that the entries s[:,:,0] and s[:,:,1] swap between turns.

  • max_time_steps (int) – Maximum number of timesteps within each episode.

  • available_actions (array of int) – Array of available actions. This list shrinks when columns saturate.

  • win_reward (1.0) – The reward associated with a win.

  • loss_reward (-1.0) – The reward associated with a loss.

  • draw_reward (0.0) – The reward associated with a draw.

close()

After the user has finished using the environment, close contains the code necessary to “clean up” the environment.

This is critical for closing rendering windows, database or HTTP connections.

render(*args, **kwargs)[source]

Render the current state of the environment.

reset()[source]

Reset the environment to the starting position.

Returns:

s (3d-array, shape: [num_rows + 1, num_cols, num_players]) – A state observation, representing the position of the current player’s tokens (s[1:,:,0]) and the other player’s tokens (s[1:,:,1]) as well as a mask over the space of actions, indicating which actions are available to the current player (s[0,:,0]) or the other player (s[0,:,1]).

Note: The “current” player is relative to whose turn it is, which means that the entries s[:,:,0] and s[:,:,1] swap between turns.

step(a)[source]

Take one step in the MDP, following the single-player convention from gymnasium.

Parameters:

a (int, options: {0, 1, 2, 3, 4, 5, 6}) – The action to be taken. The action is the zero-based count of the possible insertion slots, starting from the left of the board.

Returns:

  • s_next (array, shape [6, 7, 2]) – A next-state observation, representing the position of the current player’s tokens (s[1:,:,0]) and the other player’s tokens (s[1:,:,1]) as well as a mask over the space of actions, indicating which actions are available to the current player (s[0,:,0]) or the other player (s[0,:,1]).

    Note: The “current” player is relative to whose turn it is, which means that the entries s[:,:,0] and s[:,:,1] swap between turns.

  • r (float) – Reward associated with the transition \((s, a)\to s_\text{next}\).

    Note: Since “current” player is relative to whose turn it is, you need to be careful about aligning the rewards with the correct state or state-action pair. In particular, this reward \(r\) is the one associated with the \(s\) and \(a\), i.e. not aligned with \(s_\text{next}\).

  • done (bool) – Whether the episode is done.

  • info (dict or None) – A dict with some extra information (or None).

property np_random: Generator

Returns the environment’s internal _np_random that if not set will initialise with a random seed.

Returns:

Instances of np.random.Generator

property unwrapped: Env[ObsType, ActType]

Returns the base non-wrapped environment.

Returns:

Env – The base non-wrapped gymnasium.Env instance