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

Xingtu Liu

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arXiv · 2510.23448

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

Xingtu Liu

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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.

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Xingtu Liu. 2025-10-27. An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning. https://arxiv.org/abs/2510.23448

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