Basketball Pong¶

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

Import	`from pettingzoo.atari import basketball_pong_v3`
Actions	Discrete
Parallel API	Yes
Manual Control	No
Agents	`agents= ['first_0', 'second_0']`
Agents	2
Action Shape	(1,)
Action Values	[0,5]
Observation Shape	(210, 160, 3)
Observation Values	(0,255)

A competitive game of control.

Try to get the ball in your opponents hoop. But you cannot move on their side of the court. Scoring a point also gives your opponent -1 reward.

Serves are timed: If the player does not serve within 2 seconds of receiving the ball, they receive -1 points, and the timer resets. This prevents one player from indefinitely stalling the game, but also means it is no longer a purely zero sum game.

Official Video Olympics manual

Environment parameters¶

Some environment parameters are common to all Atari environments and are described in the base Atari documentation.

Parameters specific to Basketball_Pong are

basketball_pong_v3.env(num_players=2)

num_players: Number of players (must be either 2 or 4)

Action Space (Minimal)¶

In any given turn, an agent can choose from one of 6 actions.

Action	Behavior
0	No operation
1	Fire
2	Move up
3	Move right
4	Move left
5	Move down

Version History¶

v3: Minimal action space (1.18.0)
v2: No action timer (1.9.0)
v1: Breaking changes to entire API (1.4.0)
v0: Initial versions release (1.0.0)

Usage¶

AEC¶

from pettingzoo.atari import basketball_pong_v3

env = basketball_pong_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:
        # this is where you would insert your policy
        action = env.action_space(agent).sample()

    env.step(action)
env.close()

Parallel¶

from pettingzoo.atari import basketball_pong_v3

env = basketball_pong_v3.parallel_env(render_mode="human")
observations, infos = env.reset()

while env.agents:
    # this is where you would insert your policy
    actions = {agent: env.action_space(agent).sample() for agent in env.agents}

    observations, rewards, terminations, truncations, infos = env.step(actions)
env.close()

API¶

class pettingzoo.atari.basketball_pong.basketball_pong.raw_env(num_players=2, **kwargs)[source]¶