semvec/pypi-packages huggingface dataset:
Minigrid 2.1.0
Minimalistic gridworld reinforcement learning environments
License: Apache
Requires Python: >=3.7, <3.11
Keywords: Memory, Environment, Agent, RL, Gymnasium

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  <img src="figures/door-key-curriculum.gif" width=200 alt="Figure Door Key Curriculum">
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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](https://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](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](https://minigrid.farama.org/content/publications/), please create an issue in the [GitHub repository](https://github.com/Farama-Foundation/Minigrid/issues/new/choose).


# 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](https://minigrid.farama.org/environments/minigrid/). 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.