Review for NeurIPS paper: Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Clarity: *** Derivations in Section 3 *** While the theorems across Section 3.1 seem reasonable I would have liked some a more self-contained presentation of theorems together with proofs. Assumption 2 (Bounded adversary power) is a bit strange, and while the experimental implementation (with the norm ball around s) seems reasonable for many environments, this should probably be defined in a better way. The authors refer to the Appendix a lot and in my opinion such derivations are necessary for the reader to follow along. I cannot really follow how the authors get there. Add Plots (similar to Appendix I, Figure 12).