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

TL;DR

This paper provides an information-theoretic framework for analyzing out-of-distribution generalization in meta-reinforcement learning, deriving bounds under various distribution shifts and examining a gradient-based algorithm.

Contribution

It introduces new generalization bounds for meta-supervised learning and meta-RL, considering different distribution shifts and MDP structures, advancing theoretical understanding.

Findings

Established OOD generalization bounds for meta-supervised learning.

Formalized the generalization problem in meta-RL with bounds exploiting MDP structure.

Analyzed the performance of a gradient-based meta-RL algorithm.

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.