[2510.23448] An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning

Computer Science > Machine Learning arXiv:2510.23448 (cs) [Submitted on 27 Oct 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning Authors:Xingtu Liu View a PDF of the paper titled An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning, by Xingtu Liu View PDF HTML (experimental) Abstract:In this work, we study out-of-distribution (OOD) generalization in meta-reinforcement learning from an information-theoretic perspective. We begin by establishing OOD generalization bounds for meta-supervised learning under two distinct distribution shift scenarios: standard distribution mismatch and a broad-to-narrow training setting. Building on this foundation, we formalize the generalization problem in meta-reinforcement learning and establish fine-grained generalization bounds that exploit the structure of Markov Decision Processes. Lastly, we analyze the generalization performance of a gradient-based meta-reinforcement learning algorithm. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2510.23448 [cs.LG]   (or arXiv:2510.23448v2 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2510.23448 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xingtu Liu [view email] [v1] Mon, 27 Oct 2025 15:52:23 UTC (35 KB) [v2] Mon, 6 Apr 2026 16:08:29 UTC (36 KB) Full-text links: Access Paper: View a PDF of the pa...