Minigrid & Miniworld: Modular & Customizable Reinforcement Learning
  Environments for Goal-Oriented Tasks
Maxime Chevalier-Boisvert Bolun Dai Mark Towers Rodrigo de Lazcano Lucas Willems Salem Lahlou Suman Pal Pablo Samuel Castro Jordan Terry
We present the Minigrid and Miniworld libraries which provide a suite of
goal-oriented 2D and 3D environments. The libraries were explicitly created
with a minimalistic design paradigm to allow users to rapidly develop new
environments for a wide range of research-specific needs. As a result, both
have received widescale adoption by the RL community, facilitating research in
a wide range of areas. In this paper, we outline the design philosophy,
environment details, and their world generation API. We also showcase the
additional capabilities brought by the unified API between Minigrid and
Miniworld through case studies on transfer learning (for both RL agents and
humans) between the different observation spaces. The source code of Minigrid
and Miniworld can be found at https://github.com/Farama-Foundation/{Minigrid,
Miniworld} along with their documentation at https://{minigrid,
miniworld}.farama.org/.