Venue: NeurIPS 2022 Workshop Offline_RL

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper is similar to PLAS (Zhou et al. 2020), which learns a policy in the latent action space through the use of a generative model. Specifically, it uses normalizing flows to learn the latent action space using offline dataset. In policy optimization time, the action is extracted from the latent space, along with the CRR loss. Two differences from the PLAS paper:
(1). use bounded base distribution U[-1, 1]. instead of normal distribution, and normalized flow is used instead of VAE.
(2). in policy optimization step, CRR is used, compared with TD3.

The method is simple. The empirical part is kind of weak, and it would be great if the authors could test the performance of the method in more complicated domains, such as AntMaze. Also, it would be great if there is some ablation study on each changes the algorithm made, to see which part helps the most.

review_title: A paper about learning policies in the latent action space, to ensure conservatism

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper introduces a method for offline reinforcement learning that involves performing unconstrained off-policy reinforcement learning on the latent space produced by a normalizing flow.

The paper is incremental. The idea has been previously explored in PLAS, and this work mainly replaces a VAE with a normalizing flow. The empirical results are underwhelming. The method underperforms on medium-expert datasets, which are more challenging for offline RL.

For these reasons, I am inclined to recommend this paper for rejection from the workshop.
review_title: Review

Decision: Accept