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A review of off-policy evaluation in reinforcement learning

Author

Listed:
  • Uehara, Masatoshi
  • Shi, Chengchun
  • Kallus, Nathan

Abstract

Reinforcement learning (RL) is one of the most vibrant research frontiers in machine learning and has been recently applied to solve a number of challenging problems. In this paper, we primarily focus on off-policy evaluation (OPE), one of the most fundamental topics in RL. In recent years, a number of OPE methods have been developed in the statistics and computer science literature. We provide a discussion on the efficiency bound of OPE, some of the existing state-of-the-art OPE methods, their statistical properties and some other related research directions that are currently actively explored.

Suggested Citation

  • Uehara, Masatoshi & Shi, Chengchun & Kallus, Nathan, 2026. "A review of off-policy evaluation in reinforcement learning," LSE Research Online Documents on Economics 127940, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:127940
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    File URL: https://researchonline.lse.ac.uk/id/eprint/127940/
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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