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  • title: Oral: Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
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            Oral: Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
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            Oral: Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning

            Apr 14, 2021

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            Ming Yin

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            YB

            Yu Bai

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            YW

            Yu-Xiang Wang

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            About

            The problem of \emph{Offline Policy Evaluation} (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real life applications. Existing work on OPE mostly focus on evaluating a \emph{fixed} target policy $\pi$, which does not provide useful bounds for offline policy learning as $\pi$ will then be data-dependent. We address this problem by \emph{simultaneously} evaluating all policies in a policy class $\Pi$ --- uniform convergence in OPE --- and obtain nearly optimal erro…

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