Venue: RBCDSAI DAI 2023 Conference

Reviews:

final_score: rating: 3: Accept 
review_content: summary: The paper presents an approach to integrate safety constrains in an offline RL setting.
strengths: > The paper is well written and explains the problem and proposed solution clearly.
> The results are presented on several RL environments and the extension proposed to TD3+BC seems to perform better in most cases.

weaknesses: > While it is reasonable that states which are less frequently visited are the most uncertain ones, it doesn't necessarily imply that these are "unsafe" sates. Do we then expect cases where imposing a safety regularizer is actually hurtful to the algorithm? For instance, is this why in the Hopper/Walker environment, TD3+BC performs better than the regularized version? A detailed look at when and where regularization would help/hurt would be good to look at.
review_title: null

final_score: rating: 4: Accept (candidate for best article)
review_content: summary: The authors posit a framework for guided offline RL that  leverages a safety expert to reduce the chances of risky behavior.

strengths: - The article is well written. The problem motivation and description of their approach is concise. 
- The evaluation is thorough for a preliminary study and the methodology is detailed. 

weaknesses: It would be nice if the authors include some thoughts on usability. Specifically, what is the information shared with the expert when requesting their inputs to guide the RL

Nitpicks
- please refer to the examples (especially refnoun) in the following URL for citing articles: https://www.read.seas.harvard.edu/~kohler/latex.html#refcap. Most references are correctly cited, but some need to be rephrased. Examples include "some approaches such as [ 7] "; "methods suggested by [25, 26]"
- Please check the references. Reference 27 for SimHash has OpenAI as an author.
review_title: null

Meta-review: Thank you for submitting your work to the Deployable AI conference, which is jointly organized by the Center for Responsible AI (CeRAI) & Robert Bosch Center for Deployable AI (RBC-DSAI), IIT Madras, Aalto University, University of Helsinki, University of Jyväskylä, and Oulu University.

We are pleased to inform you that your article is accepted. We invite you to give a short presentation on your work during the conference as a Spotlight talk. The talks are 15-minutes long and you can create slides to showcase the work. Please see the abstract reviews at the end of this email and consider the reviewer feedback when preparing your presentation. 

Your spotlight talk is scheduled on 07.06.2023 14.40 - 14.55 IST (12.10 – 12.25 EET)

All presenting authors must register for the conference – please do so ASAP if you haven’t already. For more information and registration, please visit the conference page: https://rbcdsai.iitm.ac.in/DAI-2023/index.html.

Please also ensure that all authors have their profiles on openreview. 

Further details on the zoom links to present your talk will be sent out soon. 
Decision: Accept (Oral Presentation)