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We present the Minigrid and Miniworld libraries which provide a suite ofgoal-oriented 2D and 3D environments. The libraries were explicitly createdwith a minimalistic design paradigm to allow users to rapidly develop newenvironments for a wide range of research-specific needs. As a result, bothhave received widescale adoption by the RL community, facilitating research ina wide range of areas. In this paper, we outline the design philosophy,environment details, and their world generation API. We also showcase theadditional capabilities brought by the unified API between Minigrid andMiniworld through case studies on transfer learning (for both RL agents andhumans) between the different observation spaces. The source code of Minigridand Miniworld can be found at https://github.com/Farama-Foundation/{Minigrid,Miniworld} along with their documentation at https://{minigrid,miniworld}.farama.org/.

Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks