Feb. 12, 2024, 5:42 a.m. | Gresa Shala Andr\'e Biedenkapp Josif Grabocka

cs.LG updates on arXiv.org arxiv.org

We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach. HTrMRL aims to address the challenge of enabling reinforcement learning agents to perform effectively in previously unseen tasks. We demonstrate how past episodes serve as a rich source of information, which our model effectively distills and applies to new contexts. Our learned algorithm is capable of outperforming the previous state-of-the-art and provides more efficient meta-training while significantly improving generalization capabilities. Experimental results, obtained across various simulated …

agents challenge cs.ai cs.lg enabling episodes hierarchical information meta reinforcement reinforcement learning serve tasks transformers

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