From: NeurIPS.cc/2022/Workshop/Offline_RL/Paper31/Reviewer_XZSg
title: An interesting theoretical work for a hybrid RL setting.
review: This paper introduced a hybrid RL setting where an offline RL agent has the access to interact with the environment. This paper further proposed a variant of the classical Q-iteration algorithm, named Hy-Q, for the hybrid setting. Overall, this paper discussed an important setting which has received less attention in the community, and provided some interesting results.

Pros:
- This paper proves some theoretical advantages of Hy-Q.
- Hy-Q can improve the sample efficiency of online RL.

Cons:
- Hy-Q is only tested in one complex Atari game.
- As mentioned in line90, I am curious about how would the size of offline dataset affect Hy-Q.
rating: 7: Good paper, accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct