Simple Speaker Listener

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

Import	from pettingzoo.mpe import simple_speaker_listener_v4
Actions	Discrete/Continuous
Parallel API	Yes
Manual Control	No
Agents	agents=[speaker_0, listener_0]
Agents	2
Action Shape	(3),(5)
Action Values	Discrete(3),(5)/Box(0.0, 1.0, (3)), Box(0.0, 1.0, (5))
Observation Shape	(3),(11)
Observation Values	(-inf,inf)
State Shape	(14,)
State Values	(-inf,inf)

This environment is similar to simple_reference, except that one agent is the ‘speaker’ (gray) and can speak but cannot move, while the other agent is the listener (cannot speak, but must navigate to correct landmark).

Speaker observation space: [goal_id]

Listener observation space: [self_vel, all_landmark_rel_positions, communication]

Speaker action space: [say_0, say_1, say_2, say_3, say_4, say_5, say_6, say_7, say_8, say_9]

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

Arguments

simple_speaker_listener_v4.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_speaker_listener_v4

env = simple_speaker_listener_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.mpe import simple_speaker_listener_v4

env = simple_speaker_listener_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.mpe.simple_speaker_listener.simple_speaker_listener.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]