1. 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. 2. It is stated both that the multi-env model has an inevitable performance loss and that the multi-env model outperforms the single-env model due to knowledge sharing. These two statements seem to be conflicting. Please clarify. 3. In section 5.1, the single-env results, it is not clear that FRAP is only applicable in 37 of the 112 cases. As there is quite a lot of recent work on the single-env TSCP. It would have been better to compare to a less restrictive baseline. Such methods can be found in the following: a. Shabestary, Soheil Mohamad Alizadeh, and Baher Abdulhai. "Deep learning vs. discrete reinforcement learning for adaptive traffic signal control." International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. b. Ault, James, et al. "Learning an Interpretable Traffic Signal Control Policy." International Conference on Autonomous Agents and MultiAgent Systems. AAMAS, 2020. c. Liang, Xiaoyuan, et al. "Deep reinforcement learning for traffic light control in vehicular networks." IEEE Transactions on Vehicular Technology. IEEE, 2019. 4. In the supplementary material it is stated: “AttendLight in single-env regime outperforms other algorithms in 107 cases out of 112 cases“ and that AttendLight reduces ATT by 10% on average over FRAP. While it is clear how attention is useful in the multi-env setting, could you please add some analysis as to why it is expected to outperform an algorithm designed for single intersections? 5. As it is proposed in the paper that the method is suitable for city-wide control, it is important to provide an analysis of the worst-case results of the method. If on average traffic is alleviated, but certain intersections become nearly impassable this would not be a viable solution. A glance at the numbers in the supplement shows this method may result in some intersections experiencing a 78% increase in average travel time. Please provide such a worse case analysis.
2. It is stated both that the multi-env model has an inevitable performance loss and that the multi-env model outperforms the single-env model due to knowledge sharing. These two statements seem to be conflicting. Please clarify.