More Efficient Off-Policy Evaluation through Regularized Targeted Learning

Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan

Abstract: We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated b...