Information-Theoretic Generalization Bounds for Batch Reinforcement Learning

Information-Theoretic Generalization Bounds for Batch Reinforcement Learning

We analyze the generalization properties of batch reinforcement learning (batch RL) with value function approximation from an information-theoretic perspective. We derive generalization bounds for batch RL using (conditional) mutual information. In addition, we demonstrate how to establish a connect...

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Bibliographic Details
Main Author: Xingtu Liu
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Entropy
Subjects:
reinforcement learning
learning theory
generalization
mutual information
Online Access:https://www.mdpi.com/1099-4300/26/11/995
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https://www.mdpi.com/1099-4300/26/11/995

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