Information-Theoretic Considerations in Batch Reinforcement Learning

Jinglin Chen; Nan Jiang

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arXiv · 1905.00360

Information-Theoretic Considerations in Batch Reinforcement Learning

Jinglin Chen·Nan Jiang

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Abstract

Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucially rely on two types of assumptions: (1) mild distribution shift, and (2) representation conditions that are stronger than realizability. However, the necessity ("why do we need them?") and the naturalness ("when do they hold?") of such assumptions have largely eluded the literature. In this paper, we revisit these assumptions and provide theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation.

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Jinglin Chen, Nan Jiang. 2019-05-01. Information-Theoretic Considerations in Batch Reinforcement Learning. https://arxiv.org/abs/1905.00360

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