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# MiniGrid is a simple and easily configurable grid world environments for reinforcement learning


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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](https://github.com/lcswillems/torch-rl).

Requirements:
- 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:

```
@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}},
}
```

```{toctree}
:hidden:
:caption: Introduction

content/installation
content/basic_usage
api/wrappers
content/pubs
```


```{toctree}
:hidden:
:caption: Environments

environments/design
environments/index
```

```{toctree}
:hidden:
:caption: Development

Github <https://github.com/Farama-Foundation/MiniGrid>
Donate <https://farama.org/donations>
Contribute to the Docs <404>
```