From: ICML.cc/2021/Workshop/AML/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper studied adversarial attacks on deep RL policies. There are some concerns raised by the reviewers, which could be addressed in the revision.

From: ICML.cc/2021/Workshop/AML/Paper64/Reviewer_DrcP
title: The results are interesting but one-sided
review: This paper compare the perturbations generated in adversarial training agent and vanilla DRL agent in different frequencies in the Fourier domain. The empirical results shows that the adversarial-training is more vulnerable to low-frequency perturbation in Fourier domain provided by targeted attack.

Main Concerns:
1. need more victim policies and more adversarial-trained policies. I also suggest to test on MuJoCo environments.
2. other kinds of perturbations.  My main concern of this work is whether this conclusions holds for other kinds of perturbations.


rating: Accept
confidence: 5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature