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Minimalistic gridworld reinforcement learning environments

Project description

MiniGrid (formerly gym-minigrid)

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There are other gridworld Gym environments out there, but this one is designed to be particularly simple, lightweight and fast. The code has very few dependencies, making it less likely to break or fail to install. It loads no external sprites/textures, and it can run at up to 5000 FPS on a Core i7 laptop, which means you can run your experiments faster. A known-working RL implementation can be found in this repository.


  • Python 3.7 to 3.10
  • OpenAI Gym v0.26
  • NumPy 1.18+
  • Matplotlib (optional, only needed for display) - 3.0+

Please use this bibtex if you want to cite this repository in your publications:

  author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
  title = {Minimalistic Gridworld Environment for OpenAI Gym},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}},

List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):

This environment has been built as part of work done at Mila. The Dynamic obstacles environment has been added as part of work done at IAS in TU Darmstadt and the University of Genoa for mobile robot navigation with dynamic obstacles.


There is now a pip package available, which is updated periodically:

pip3 install gym-minigrid

Alternatively, to get the latest version of MiniGrid, you can clone this repository and install the dependencies with pip3:

git clone
cd gym-minigrid
pip3 install -e .

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