Simple Reference¶
This environment is part of the MPE environments. Please read that page first for general information.
Import |
|
|---|---|
Actions |
Discrete/Continuous |
Parallel API |
Yes |
Manual Control |
No |
Agents |
|
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) |
This environment has 2 agents and 3 landmarks of different colors. Each agent wants to get closer to their target landmark, which is known only by the other agents. Both agents are simultaneous speakers and listeners.
Locally, the agents are rewarded by their distance to their target landmark. Globally, all agents are rewarded by the average distance of all the agents to their respective landmarks. The relative weight of these rewards is controlled by the local_ratio parameter.
Agent observation space: [self_vel, all_landmark_rel_positions, landmark_ids, goal_id, communication]
Agent discrete action space: [say_0, say_1, say_2, say_3, say_4, say_5, say_6, say_7, say_8, say_9] X [no_action, move_left, move_right, move_down, move_up]
Where X is the Cartesian product (giving a total action space of 50).
Agent continuous action space: [no_action, move_left, move_right, move_down, move_up, say_0, say_1, say_2, say_3, say_4, say_5, say_6, say_7, say_8, say_9]
Arguments¶
simple_reference_v3.env(local_ratio=0.5, max_cycles=25, continuous_actions=False, dynamic_rescaling=False)
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
dynamic_rescaling: Whether to rescale the size of agents and landmarks based on the screen size
Usage¶
AEC¶
from pettingzoo.mpe import simple_reference_v3
env = simple_reference_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_reference_v3
env = simple_reference_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()