minigrid

Minigrid is a versatile library that offers a collection of lightweight, customizable 2D grid-world environments for conducting reinforcement learning research. These environments, built following the Gymnasium API, feature simple agents with discrete action spaces navigating through grid-like maps filled with various obstacles such as walls, lava, and dynamic objects. Each environment presents goal-oriented tasks, from basic object interactions like picking up boxes or unlocking doors, to more complex missions like navigating mazes. The environments are easily adjustable in terms of size and complexity, making them ideal for curriculum learning and experimentation. Designed for speed and simplicity, Minigrid is widely used for reinforcement learning research due to its minimal computational overhead and ease of customization.

In addition to the core Minigrid environments, the library also includes the BabyAI environment collection, which focuses on grounded language learning. In these scenarios, agents are provided with natural language instructions, such as “place the red ball next to the box,” and must complete tasks by interacting with their surroundings. These environments build upon the core grid-world framework but add a layer of complexity through language-driven tasks, making them valuable for research in language-based reasoning and multi-modal learning. Both Minigrid and BabyAI environments have been referenced in numerous publications, contributing to a broad range of reinforcement learning advancements.