XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Artem Sergeevich Agarkov, Viacheslav Sinii, Sergey Kolesnikov

Keywords: reinforcement learning, meta-reinforcement learning, jax accelerated environments, xland

TL;DR: We present XLand-Minigrid, a suite of tools, benchmarks and grid-world environments for meta-RL research in JAX

Abstract: Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed to be highly scalable and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. Along with the environments, XLand-MiniGrid provides pre-sampled benchmarks with millions of unique tasks of varying difficulty and easy-to-use baselines that allow users to quickly start training adaptive agents. In addition, we have conducted a preliminary analysis of scaling and generalization, showing that our baselines are capable of reaching millions of steps per second during training and validating that the proposed benchmarks are challenging. XLand-MiniGrid is open-source and available at \url{https://github.com/corl-team/xland-minigrid}.

Submission Number: 1574