Venue: NeurIPS 2022 Workshop Offline_RL

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: 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.
review_title: Well thought of benchmarks for an important problem

final_score: rating: 7: Good paper, accept
review_content: 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.
review_title: Official Review

Decision: Accept