Venue: AAAI 2022 Workshop AdvML

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This work proposes Constrained Randomization of Policy (CRoP) as a deep reinforcement learning defence method against unauthorized replication attacks. Though the theoretical part needs to be organized better, the extensive experiments show the effectiveness of CRoP.

Some suggestions:

1. It's better and more clear to first define the attack objective as well as the defence objective. 

2. Some symbols can be more concise, e.g. in eq(1), $\!\exists \hat{a}\in\hat{A}$ may be replaced with $\hat{A} = \emptyset$.

3. It's better to define the meaning of the symbol before it's first used, e.g. $m_n$.

4. Some theoretical results need more careful discussion, e.g. eq(9) provides an upper bound, what's the connection with defence? 

review_title: A good work for improving the robustness against unauthorized replication attacks

Meta-review: The reviewer agrees to accept this paper. Please address the reviewer's comment in camera-ready. Please format your paper within 4 pages (excluding references) also.
Decision: Accept (Short Paper)