Python reinforcement-learning environment openai-gym
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A collection of multi agent environments based on OpenAI gym.

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A collection of multi agent environments based on OpenAI gym.

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cd ma-gym
pip install -e .


import gym
import ma_gym

env = gym.make('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


  • [x] Checkers
  • [x] Combat
  • [x] PredatorPrey
  • [x] Pong Duel
    (two player pong game)
  • [x] Switch
  • [x] Lumberjacks
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.


| Checkers-v0 | Combat-v0 | Lumberjacks-v0 | |:---:|:---:|:---:| |Checkers-v0.gif|Combat-v0.gif|Lumberjacks-v0.gif| | PongDuel-v0 | PredatorPrey5x5-v0 | PredatorPrey7x7-v0 | | PongDuel-v0.gif | PredatorPrey5x5-v0.gif | PredatorPrey7x7-v0.gif | | Switch2-v0 | Switch4-v0 | | | Switch2-v0.gif | Switch4-v0.gif | |


  • Install:
    pip install pytest
  • Run:


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

      author = {Koul, Anurag},
      title = {ma-gym: Collection of multi-agent environments based on OpenAI gym.},
      year = {2019},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{}},


This project was developed to complement my research internship @ SAS.

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