Simple World Comm
This environment is part of the mpe environments. Please read that page first for general information.
| Name | Value |
|---|---|
| Actions | Discrete |
| Agents | 6 |
| Parallel API | Yes |
| Manual Control | No |
| Action Shape | (5),(20) |
| Action Values | Discrete(5),(20) |
| Observation Shape | (28),(34) |
| Observation Values | (-inf,inf) |
| Import | from pettingzoo.mpe import simple_world_comm_v2 |
| Agents | agents=[leadadversary_0, adversary_0, adversary_1, adversary_3, agent_0, agent_1] |
Agent Environment Cycle
Simple World Comm
This environment is similar to simple_tag, except there is food (small blue balls) that the good agents are rewarded for being near, there are ‘forests’ that hide agents inside from being seen, and there is a ‘leader adversary’ that can see the agents at all times and can communicate with the other adversaries to help coordinate the chase. By default, there are 2 good agents, 3 adversaries, 1 obstacles, 2 foods, and 2 forests.
In particular, the good agents reward, is -5 for every collision with an adversary, -2 x bound by the bound function described in simple_tag, +2 for every collision with a food, and -0.05 x minimum distance to any food. The adversarial agents are rewarded +5 for collisions and -0.1 x minimum distance to a good agent. s
Good agent observations: [self_vel, self_pos, landmark_rel_positions, other_agent_rel_positions, other_agent_velocities, self_in_forest]
Normal adversary observations:[self_vel, self_pos, landmark_rel_positions, other_agent_rel_positions, other_agent_velocities, self_in_forest, leader_comm]
Adversary leader observations: [self_vel, self_pos, landmark_rel_positions, other_agent_rel_positions, other_agent_velocities, leader_comm]
Note that when the forests prevent an agent from being seen, the observation of that agents relative position is set to (0,0).
Good agent action space: [no_action, move_left, move_right, move_down, move_up]
Normal adversary action space: [no_action, move_left, move_right, move_down, move_up]
Adversary leader observation space: [say_0, say_1, say_2, say_3] X [no_action, move_left, move_right, move_down, move_up]
Where X is the Cartesian product (giving a total action space of 50).
Arguments
simple_world_comm.env(num_good=2, num_adversaries=4, num_obstacles=1,
num_food=2, max_cycles=25, num_forests=2)
num_good: number of good agents
num_adversaries: number of adversaries
num_obstacles: number of obstacles
num_food: number of food locations that good agents are rewarded at
max_cycles: number of frames (a step for each agent) until game terminates
num_forests: number of forests that can hide agents inside from being seen