ma-gym

It's a collection of multi agent environments based on OpenAI gym. Also, you can use minimal-marl to warm-start training of agents.

Installation

Using PyPI:

pip install ma-gym

Directly from source (recommended):

git clone https://github.com/koulanurag/ma-gym.git
cd ma-gym
pip install -e .

Reference:

Please use this bibtex if you would like to cite it:

@misc{magym,
      author = {Koul, Anurag},
      title = {ma-gym: Collection of multi-agent environments based on OpenAI gym.},
      year = {2019},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/koulanurag/ma-gym}},
    }

Usage:

import gym

env = gym.make('ma_gym:Switch2-v0')
done_n = [False for _ in range(env.n_agents)]
ep_reward = 0

obs_n = env.reset()
while not all(done_n):
    env.render()
    obs_n, reward_n, done_n, info = env.step(env.action_space.sample())
    ep_reward += sum(reward_n)
env.close()

Please refer to Wiki for complete usage details

Environments:

• Checkers
• Combat
• PredatorPrey
• Pong Duel (two player pong game)
• Switch
• Lumberjacks
• TrafficJunction

Note : openai's environment can be accessed in multi agent form by prefix "ma_".Eg: ma_CartPole-v0
This returns an instance of CartPole-v0 in "multi agent wrapper" having a single agent. 
These environments are helpful during debugging.

Please refer to Wiki for more details.

Zoo!

Checkers-v0	Combat-v0	Lumberjacks-v0
PongDuel-v0	PredatorPrey5x5-v0	PredatorPrey7x7-v0
Switch2-v0	Switch4-v0	TrafficJunction4-v0
TrafficJunction10-v0

Testing:

• Install: pip install -e ".[test]"
• Run: pytest

Acknowledgement:

• This project was initially developed to complement my research internship @ SAS (Summer - 2019).