Simple

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

Import	from pettingzoo.mpe import simple_v3
Actions	Discrete/Continuous
Parallel API	Yes
Manual Control	No
Agents	agents= [agent_0]
Agents	1
Action Shape	(5)
Action Values	Discrete(5)/Box(0.0, 1.0, (5,))
Observation Shape	(4)
Observation Values	(-inf,inf)
State Shape	(4,)
State Values	(-inf,inf)

In this environment a single agent sees a landmark position and is rewarded based on how close it gets to the landmark (Euclidean distance). This is not a multiagent environment, and is primarily intended for debugging purposes.

Observation space: [self_vel, landmark_rel_position]

Arguments

simple_v3.env(max_cycles=25, continuous_actions=False)

max_cycles: number of frames (a step for each agent) until game terminates

continuous_actions: Whether agent action spaces are discrete(default) or continuous

Usage

AEC

from pettingzoo.mpe import simple_adversary_v3

env = simple_adversary_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.mpe import simple_adversary_v3

env = simple_adversary_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.mpe.simple.simple.raw_env(max_cycles=25, continuous_actions=False, render_mode=None)

action_spaces: dict[AgentID, gymnasium.spaces.Space]

agent_selection: AgentID

agents: list[AgentID]

infos: dict[AgentID, dict[str, Any]]

observation_spaces: dict[AgentID, gymnasium.spaces.Space]

possible_agents: list[AgentID]

rewards: dict[AgentID, float]

terminations: dict[AgentID, bool]

truncations: dict[AgentID, bool]