Simple Adversary

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

Import	from pettingzoo.mpe import simple_adversary_v3
Actions	Discrete/Continuous
Parallel API	Yes
Manual Control	No
Agents	agents= [adversary_0, agent_0,agent_1]
Agents	3
Action Shape	(5)
Action Values	Discrete(5)/Box(0.0, 1.0, (5))
Observation Shape	(8),(10)
Observation Values	(-inf,inf)
State Shape	(28,)
State Values	(-inf,inf)

In this environment, there is 1 adversary (red), N good agents (green), N landmarks (default N=2). All agents observe the position of landmarks and other agents. One landmark is the ‘target landmark’ (colored green). Good agents are rewarded based on how close the closest one of them is to the target landmark, but negatively rewarded based on how close the adversary is to the target landmark. The adversary is rewarded based on distance to the target, but it doesn’t know which landmark is the target landmark. All rewards are unscaled Euclidean distance (see main MPE documentation for average distance). This means good agents have to learn to ‘split up’ and cover all landmarks to deceive the adversary.

Agent observation space: [self_pos, self_vel, goal_rel_position, landmark_rel_position, other_agent_rel_positions]

Adversary observation space: [landmark_rel_position, other_agents_rel_positions]

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

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

Arguments

simple_adversary_v3.env(N=2, max_cycles=25, continuous_actions=False)

N: number of good agents and landmarks

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_adversary.simple_adversary.raw_env(N=2, 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]