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arXiv · 2010.11652

CoinDICE: Off-Policy Confidence Interval Estimation

Bo Dai·Ofir Nachum·Yinlam Chow·Lihong Li·Csaba Szepesvári·Dale Schuurmans
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Abstract

We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given only access to a static experience dataset collected by unknown behavior policies. Starting from a function space embedding of the linear program formulation of the $Q$-function, we obtain an optimization problem with generalized estimating equation constraints. By applying the generalized empirical likelihood method to the resulting Lagrangian, we propose CoinDICE, a novel and efficient algorithm for computing confidence intervals. Theoretically, we prove the obtained confidence intervals are valid, in both asymptotic and finite-sample regimes. Empirically, we show in a variety of benchmarks that the confidence interval estimates are tighter and more accurate than existing methods.

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Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans. 2020-10-22. CoinDICE: Off-Policy Confidence Interval Estimation. https://arxiv.org/abs/2010.11652

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