From: NeurIPS.cc/2022/Workshop/Offline_RL/Paper48/Reviewer_CuYE
title: Well thought of benchmarks for an important problem
review: The authors introduce benchmarks, metrics and baselines for policy ranking approaches that also produce confidence levels. Reliability is a very important problem in offline policy evaluation so I am glad to see this being addressed. The metrics, datasets and baselines proposed make sense to me. I was going to mention that FQE with ensembles would be a good next baseline to try but I think the authors are right that in the context of comparing lots of policies it gets quite computationally expensive. Anyway, all in all a very solid paper that I am happy to see.
rating: 9: Top 15% of accepted papers, strong accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct

From: NeurIPS.cc/2022/Workshop/Offline_RL/Paper48/Reviewer_38o4
title: Official Review
review: This paper proposed baselines and benchmarks for offline policy comparison with confidence. I believe the authors studied an important topic in offline RL, and their contribution will enlighten and make convenience for the future work along this direction. Therefore, I recommend the acceptance of this paper.
rating: 7: Good paper, accept
confidence: 3: The reviewer is fairly confident that the evaluation is correct