A Review of Off-Policy Evaluation in Reinforcement Learning

12/13/2022
by   Masatoshi Uehara, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset