XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
2023
Online
report
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.
Comment: NeurIPS 2023, Workshop, Source code: https://github.com/corl-team/xland-minigrid
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XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
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Autor/in / Beteiligte Person: | Nikulin, Alexander ; Kurenkov, Vladislav ; Zisman, Ilya ; Agarkov, Artem ; Sinii, Viacheslav ; Kolesnikov, Sergey |
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Veröffentlichung: | 2023 |
Medientyp: | report |
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