Gym-MiniGrid/docs/index.md

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Minigrid (formerly gym-minigrid) contains simple and easily configurable grid world environments for reinforcement learning

There are other gridworld Gymnasium 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.

Requirements:

• Python 3.7 to 3.10
• Gymnasium 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:

@misc{minigrid,
  author = {Chevalier-Boisvert, Maxime and Willems, Lucas and Pal, Suman},
  title = {Minimalistic Gridworld Environment for Gymnasium},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Farama-Foundation/MiniGrid}},
}

Installation

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

pip install minigrid

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

git clone https://github.com/Farama-Foundation/MiniGrid
cd MiniGrid
pip install -e .

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:caption: Introduction

content/basic_usage
content/publications

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:caption: Wrappers

api/wrapper

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:caption: Environments

environments/design
environments/index
environments/babyAI_index

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:caption: Development

Github <https://github.com/Farama-Foundation/MiniGrid>