Review for NeurIPS paper: Zap Q-Learning With Nonlinear Function Approximation

Summary and Contributions: This paper introduces a version of Zap Q-learning that can be applied to arbitrary approximation architectures for Q-functions. Convergence analysis is undertaken, and a version of the algorithm with MLP function approximators is applied to several classical control tasks. POST-REBUTTAL ------------------------ I thank the authors for their response. I appreciate the comments around reorganisation of material, and clarification of some of the technical points I raised. There are two main concerns that I have with the paper that prevent me from strongly recommending acceptance, described below.