Combined Arms

environment gif

This environment is part of the magent environments. Please read that page first for general information.

Name Value
Actions Discrete
Agents 162
Parallel API Yes
Manual Control No
Action Shape (9),(25)
Action Values Discrete(9),(25)
Observation Shape (13,13,35), (13,13,51)
Observation Values [0,2]
Import pettingzoo.magent import combined_arms_v3
Agents agents= [redmelee_[0-44], redranged_[0-35], bluemelee_[0-44], blueranged_[0-35]]

Agent Environment Cycle

environment aec diagram

Combined Arms

A large-scale team battle. Here there are two types of agents on each team, ranged units which can attack father and move faster but have less HP, and melee units which can only attack close units and move more slowly but have more HP. Unlike battle and battlefield, agents can attack units on their own team (they just are not rewarded for doing so). Agents slowly regain HP over time, so it is best to kill an opposing agent quickly. Specifically, agents have 10 HP, are damaged 2 HP by each attack, and recover 0.1 HP every turn.

Action Space

Key: move_N means N separate actions, one to move to each of the N nearest squares on the grid.

Melee action options: [do_nothing, move_4, attack_4]

Ranged action options: [do_nothing, move_12, attack_12]

Reward

Reward is given as:

If multiple options apply, rewards are added.

Observation space

The observation space is a 13x13 map with 35 channels for Melee and 51 channels for Ranged units, which are (in order):

name number of channels
obstacle/off the map 1
my_team_presence 1
my_team_hp 1
my_team_minimap 1
Other teams presences/heaths/minimaps (in some order) 9
binary_agent_id 10
one_hot_action 9 Melee/25 ranged
last_reward 1
agent_position 2

Arguments

combined_arms_v3.env(map_size=45, minimap_mode=True, step_reward=-0.005, dead_penalty=-0.1, attack_penalty=-0.1, attack_opponent_reward=0.2, max_cycles=1000)

map_size: Sets dimensions of the (square) map. Increasing the size increases the number of agents. Minimum size is 16.

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).

step_reward: reward after every step

dead_penalty: reward when killed

attack_penalty: reward when attacking anything

attack_opponent_reward: reward added for attacking an opponent

max_cycles: number of cycles (a step for each agent) until game terminates