A Review of Off-Policy Evaluation in Reinforcement Learning
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.
Fields of Study & Subjects
Reinforcement Learning in Robotics
Publication details
- Journal / source
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Volume
- 41
- Issue
- 3
- Pages
- 561–581
- Publication date
- 31 July 2026
- Language
- EN