Pong#

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

Import	`from pettingzoo.atari import 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)

Classic two player competitive game of timing.

Get the ball past the opponent.

Scoring a point gives you +1 reward and 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 Pong are

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 right
3	Move left
4	Fire right
5	Fire left

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.pong.pong.raw_env(num_players=2, game_version='classic', **kwargs)[source]#