NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:1845
Title:Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning

This paper presents new estimators for Off Policy Evaluation (OPE) based on likelihoods and argues that the new estimators are better than Importance Sampling (IS). The paper provides strong theoretical guarantees of the estimators, and demonstrates their through simple experiments. The reviewers agree that the paper is well written overall and the proposed methods are technically sound and likely to be built upon by the community. One reviewer is unsure if the proposed methods will be practical in RL applications. The experiments are performed on very simple tasks. The authors should improve this particular point to make a good impact.