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Workshop: Intrinsically Motivated Open-ended Learning (IMOL) Workshop

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

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

Keywords: [ jax accelerated environments ] [ Meta-Reinforcement Learning ] [ Reinforcement Learning ] [ xland ]

Abstract: We present XLand-Minigrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. To demonstrate the generality of our library, we have implemented some well-known single-task environments as well as new meta-learning environments capable of generating $10^8$ distinct tasks. We have empirically shown that the proposed environments can scale up to $2^{13}$ parallel instances on the GPU, reaching tens of millions of steps per second.

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