Simple Spread

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

Import	from pettingzoo.mpe import simple_spread_v3
Actions	Discrete/Continuous
Parallel API	Yes
Manual Control	No
Agents	agents= [agent_0, agent_1, agent_2]
Agents	3
Action Shape	(5)
Action Values	Discrete(5)/Box(0.0, 1.0, (5))
Observation Shape	(18)
Observation Values	(-inf,inf)
State Shape	(54,)
State Values	(-inf,inf)

This environment has N agents, N landmarks (default N=3). At a high level, agents must learn to cover all the landmarks while avoiding collisions.

More specifically, all agents are globally rewarded based on how far the closest agent is to each landmark (sum of the minimum distances). Locally, the agents are penalized if they collide with other agents (-1 for each collision). The relative weights of these rewards can be controlled with the local_ratio parameter.

Agent observations: [self_vel, self_pos, landmark_rel_positions, other_agent_rel_positions, communication]

Agent action space: [no_action, move_left, move_right, move_down, move_up]

Arguments

simple_spread_v3.env(N=3, local_ratio=0.5, max_cycles=25, continuous_actions=False)

N: number of agents and landmarks

local_ratio: Weight applied to local reward and global reward. Global reward weight will always be 1 - local reward weight.

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_spread_v3

env = simple_spread_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_spread_v3

env = simple_spread_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_spread.simple_spread.raw_env(N=3, local_ratio=0.5, 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]