Quadrapong#

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

Import	`from pettingzoo.atari import quadrapong_v4`
Actions	Discrete
Parallel API	Yes
Manual Control	No
Agents	`agents= ['first_0', 'second_0', 'third_0', 'fourth_0']`
Agents	4
Action Shape	(1,)
Action Values	[0,5]
Observation Shape	(210, 160, 3)
Observation Values	(0,255)

Four player team battle.

Each player controls a paddle and defends a scoring area. However, this is a team game, and so two of the 4 scoring areas belong to the same team. So a given team must try to coordinate to get the ball away from their scoring areas towards their opponent’s. Specifically first_0 and third_0 are on one team and second_0 and fourth_0 are on the other.

Scoring a point gives your team +1 reward and your opponent team -1 reward.

Serves are timed: If the player does not serve within 2 seconds of receiving the ball, their team receives -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#

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

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#

v4: Minimal Action Space (1.18.0)
v3: No action timer (1.9.0)
v1: Breaking changes to entire API (1.4.0)
v2: Fixed quadrapong rewards (1.2.0)
v0: Initial versions release (1.0.0)

Usage#

AEC#

from pettingzoo.atari import quadrapong_v4

env = quadrapong_v4.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 quadrapong_v4

env = quadrapong_v4.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.quadrapong.quadrapong.raw_env(**kwargs)[source]#