Review for NeurIPS paper: AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

The primary motivation for the work is not well supported. Certainly, cities do manage thousands of intersections. While unquantified, it is not clear that the cost of training individually would surpass that of the degradation seen in the multi-env setting. These two statements seem to be conflicting. In section 5.1, the single-env results, it is not clear that FRAP is only applicable in 37 of the 112 cases.