More Efficient Off-Policy Evaluation through Regularized Targeted Learning

pp. 654-663, 2019.

Cited by: 7|Views5
EI

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 by a different policy, or policies. In particular, we introduce a novel doubly-robust estimator for the OPE problem in RL, based on the Target...More

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