An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910104041816064 |
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| author | Liu, Xingtu |
| author_facet | Liu, Xingtu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning Liu, Xingtu Machine Learning 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. |
| title | An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.23448 |