Adversarial Pursuit
This environment is part of the magent environments. Please read that page first for general information.
| Name | Value |
|---|---|
| Actions | Discrete |
| Agents | 75 |
| Parallel API | True |
| Manual Control | No |
| Action Shape | (9),(13) |
| Action Values | Discrete(9),(13) |
| Observation Shape | (9,9,15), (10,10,19) |
| Observation Values | [0,2] |
| Import | pettingzoo.magent import adversarial_pursuit_v1 |
| Agents | agents= [predator_[0-24], prey_[0-49]] |
Agent Environment Cycle
Adversarial Pursuit
The red agents must navigate the obstacles and attack the blue agents. The blue agents should try to avoid being attacked. Since the red agents are slower (but larger) than the blue agents, they must work together to trap the blue agents, so they can attack them continually (note that they blue agent’s won’t die if attacked, so they can be used as an infinite source of reward).
Predator action options: [do_nothing, move_4, attack_8]
Predator’s reward is given as:
- 1 reward for attacking a prey
- -0.2 reward for attacking (attack_penalty option)
Prey action options: [do_nothing, move_8]
Prey’s reward is given as:
- -1 reward for being attacked
Observation space: [empty, obstacle, predators, prey, one_hot_action, last_reward]
Map size: 45x45
Arguments
adversarial_pursuit_v1.env(attack_penalty=-0.2, max_frames=500)
attack_penalty: Adds the following value to the reward whenever an attacking action is taken
max_frames: number of frames (a step for each agent) until game terminates