Adversarial Pursuit

environment gif

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 (9,9,15), (10,10,19)
Observation Values [0,2]
Import pettingzoo.magent import adversarial_pursuit_v2
Agents agents= [predator_[0-24], prey_[0-49]]

Agent Environment Cycle

environment aec diagram

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]

Reward

Predator’s reward is given as:

Prey action options: [do_nothing, move_8]

Prey’s reward is given as:

Observation space: [obstacle, my_team_presence, my_team_presence_health, other_team_presence, other_team_presence_health, one_hot_action, last_reward]

Arguments

adversarial_pursuit_v2.env(map_size=45, minimap_mode=False, tag_penalty=-0.2, max_cycles=500)

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