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.
@article{arxiv.2212.06355,
title = {A Review of Off-Policy Evaluation in Reinforcement Learning},
author = {Masatoshi Uehara and Chengchun Shi and Nathan Kallus},
journal= {arXiv preprint arXiv:2212.06355},
year = {2022}
}