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arXiv · 2312.12044

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

Alexander Nikulin·Vladislav Kurenkov·Ilya Zisman·Artem Agarkov·Viacheslav Sinii·Sergey Kolesnikov
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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 https://github.com/dunnolab/xland-minigrid.

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Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Artem Agarkov, Viacheslav Sinii, Sergey Kolesnikov. 2023-12-19. XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX. https://arxiv.org/abs/2312.12044

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