Connect Four#

../../../_images/classic_connect_four.gif

This environment is part of the classic environments. Please read that page first for general information.

Import

from pettingzoo.classic import connect_four_v3

Actions

Discrete

Parallel API

Yes

Manual Control

No

Agents

agents= ['player_0', 'player_1']

Agents

2

Action Shape

(1,)

Action Values

Discrete(7)

Observation Shape

(6, 7, 2)

Observation Values

[0,1]

Connect Four is a 2-player turn based game, where players must connect four of their tokens vertically, horizontally or diagonally. The players drop their respective token in a column of a standing grid, where each token will fall until it reaches the bottom of the column or reaches an existing token. Players cannot place a token in a full column, and the game ends when either a player has made a sequence of 4 tokens, or when all 7 columns have been filled.

Observation Space#

The observation is a dictionary which contains an 'observation' element which is the usual RL observation described below, and an 'action_mask' which holds the legal moves, described in the Legal Actions Mask section.

The main observation space is 2 planes of a 6x7 grid. Each plane represents a specific agent’s tokens, and each location in the grid represents the placement of the corresponding agent’s token. 1 indicates that the agent has a token placed in that cell, and 0 indicates they do not have a token in that cell. A 0 means that either the cell is empty, or the other agent has a token in that cell.

Action Space#

The action space is the set of integers from 0 to 6 (inclusive), where the action represents which column a token should be dropped in.

Rewards#

If an agent successfully connects four of their tokens, they will be rewarded 1 point. At the same time, the opponent agent will be awarded -1 points. If the game ends in a draw, both players are rewarded 0.

Version History#

  • v3: Fixed bug in arbitrary calls to observe() (1.8.0)

  • v2: Legal action mask in observation replaced illegal move list in infos (1.5.0)

  • v1: Bumped version of all environments due to adoption of new agent iteration scheme where all agents are iterated over after they are done (1.4.0)

  • v0: Initial versions release (1.0.0)

Usage#

AEC#

from pettingzoo.classic import connect_four_v3

env = connect_four_v3.env(render_mode="human")
env.reset(seed=42)

for agent in env.agent_iter():
    observation, reward, termination, truncation, info = env.last()

    if termination or truncation:
        action = None
    else:
        mask = observation["action_mask"]
        # this is where you would insert your policy
        action = env.action_space(agent).sample(mask)

    env.step(action)
env.close()

API#

class pettingzoo.classic.connect_four.connect_four.env(**kwargs)[source]#
class pettingzoo.classic.connect_four.connect_four.raw_env(render_mode: str | None = None, screen_scaling: int = 9)[source]#
action_space(agent)[source]#

Takes in agent and returns the action space for that agent.

MUST return the same value for the same agent name

Default implementation is to return the action_spaces dict

close()[source]#

Closes any resources that should be released.

Closes the rendering window, subprocesses, network connections, or any other resources that should be released.

observation_space(agent)[source]#

Takes in agent and returns the observation space for that agent.

MUST return the same value for the same agent name

Default implementation is to return the observation_spaces dict

observe(agent)[source]#

Returns the observation an agent currently can make.

last() calls this function.

render()[source]#

Renders the environment as specified by self.render_mode.

Render mode can be human to display a window. Other render modes in the default environments are ‘rgb_array’ which returns a numpy array and is supported by all environments outside of classic, and ‘ansi’ which returns the strings printed (specific to classic environments).

reset(seed=None, options=None)[source]#

Resets the environment to a starting state.

step(action)[source]#

Accepts and executes the action of the current agent_selection in the environment.

Automatically switches control to the next agent.