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 | Yes |
| Manual Control | No |
| Action Shape | (9),(13) |
| Action Values | Discrete(9),(13) |
| Observation Shape | (9,9,5), (10,10,9) |
| Observation Values | [0,2] |
| Import | pettingzoo.magent import adversarial_pursuit_v2 |
| Agents | agents= [predator_[0-24], prey_[0-49]] |
Agent Environment Cycle
Adversarial Pursuit
The red agents must navigate the obstacles and tag (similar to attacking, but without damaging) the blue agents. The blue agents should try to avoid being tagged. To be effective, the red agents, who are much are slower and larger than the blue agents, must work together to trap blue agents so they can be tagged continually.
Action Space
Key: move_N means N separate actions, one to move to each of the N nearest squares on the grid.
Predator action options: [do_nothing, move_4, tag_8]
Prey action options: [do_nothing, move_8]
Reward
Predator’s reward is given as:
- 1 reward for tagging a prey
- -0.2 reward for tagging anywhere (
tag_penaltyoption)
Prey’s reward is given as:
- -1 reward for being tagged
Observation space
The observation space is a 10x10 map for pursuers and a 9x9 map for the pursued. The contain the following channels, which are (in order):
| name | number of channels |
|---|---|
| obstacle/off the map | 1 |
| my_team_presence | 1 |
| my_team_hp | 1 |
| other_team_presence | 1 |
| other_team_hp | 1 |
| binary_agent_id(extra_features=True) | 10 |
| one_hot_action(extra_features=True) | 9/Prey,13/Predator |
| last_reward(extra_features=True) | 1 |
Arguments
adversarial_pursuit_v2.env(map_size=45, minimap_mode=False, tag_penalty=-0.2, max_cycles=500, extra_features=False)
map_size: Sets dimensions of the (square) map. Increasing the size increases the number of agents. Minimum size is 7.
minimap_mode: Turns on global minimap observations. These observations include your and your opponents piece densities binned over the 2d grid of the observation space. Also includes your agent_position, the absolute position on the map (rescaled from 0 to 1).
tag_penalty: reward when red agents tag anything
max_cycles: number of frames (a step for each agent) until game terminates
extra_features: Adds additional features to observation (see table). Default False