The Minigrid library contains a collection of discrete grid-world environments to conduct research on Reinforcement Learning. The environments follow the Gymnasium standard API and they are designed to be lightweight, fast, and easily customizable.

The documentation website is at minigrid.farama.org, and we have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/B8ZJ92hu

Note that the library was previously known as gym-minigrid and it has been referenced in several publications. If your publication uses the Minigrid library and you wish for it to be included in the list of publications, please create an issue in the GitHub repository.

Installation

To install the Minigrid library use pip install minigrid.

We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.

Environments

The included environments can be divided in two groups. The original Minigrid environments and the BabyAI environments.

Minigrid

The list of the environments that were included in the original Minigrid library can be found in the documentation. These environments have in common a triangle-like agent with a discrete action space that has to navigate a 2D map with different obstacles (Walls, Lava, Dynamic obstacles) depending on the environment. The task to be accomplished is described by a mission string returned by the observation of the agent. These mission tasks include different goal-oriented and hierarchical missions such as picking up boxes, opening doors with keys or navigating a maze to reach a goal location. Each environment provides one or more configurations registered with Gymansium. Each environment is also programmatically tunable in terms of size/complexity, which is useful for curriculum learning or to fine-tune difficulty.