README.md

Toy Multi agent environment

Start of a toy gym environment bank for multi-agent reinforcement learning.

This repo contains for the moment a first environment:

How to install ?

git clone https://github.com/MehdiZouitine/gym_ma_toy
cd gym_ma_toy
pip install -e .

How to use it ?

import gym_ma_toy
import gym

env = env = gym.make('team_catcher-v0')

obs = env.reset()
done = False
while not done:
    env.render()
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action)
env.close()

Team catcher:

This is a map where targets are randomly placed. The objective of the agents is that there are at least two agents on an adjacent cell of a target to catch it. When the target is caught the environment returns a reward point. The episode ends when there is no more target on the map.

The number of agent and target can be arbitrarily large.

Running multiple environment in parallel

# Running 8 environment in parallel
import gym
import gym_ma_toy

env = gym.vector.make('team_catcher-v0',num_envs=8, asynchronous=True)  

Test

pytest test/

Cite the environment as:

@misc{amarl2020
 Author = {Mehdi Zouitine, Adil Zouitine},
 Title = {Toy environment set for multi-agent reinforcement learning and more},
 Year = {2020},
}

License

This project is free and open-source software licensed under the MIT license.