Hierarchical Transformers are Efficient Meta-Reinforcement Learners

• URL: http://arxiv.org/abs/2402.06402v1
• Date: Fri, 9 Feb 2024 13:40:11 GMT
• Title: Hierarchical Transformers are Efficient Meta-Reinforcement Learners
• Authors: Gresa Shala, Andr\'e Biedenkapp, Josif Grabocka
• Abstract summary: We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach. We demonstrate how past episodes serve as a rich source of information, which our model effectively distills and applies to new contexts.
• Score: 19.79721574250755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 tasks of the Meta-World Benchmark, indicate a significant improvement in learning efficiency and adaptability compared to the state-of-the-art on a variety of tasks. Our approach not only enhances the agent's ability to generalize from limited data but also paves the way for more robust and versatile AI systems.

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