Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip S. Thomas; Emma Brunskill

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arXiv · 1604.00923

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip S. Thomas·Emma Brunskill

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Abstract

In this paper we present a new way of predicting the performance of a reinforcement learning policy given historical data that may have been generated by a different policy. The ability to evaluate a policy from historical data is important for applications where the deployment of a bad policy can be dangerous or costly. We show empirically that our algorithm produces estimates that often have orders of magnitude lower mean squared error than existing methods---it makes more efficient use of the available data. Our new estimator is based on two advances: an extension of the doubly robust estimator (Jiang and Li, 2015), and a new way to mix between model based estimates and importance sampling based estimates.

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Philip S. Thomas, Emma Brunskill. 2016-04-04. Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. https://arxiv.org/abs/1604.00923

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