Review for NeurIPS paper: Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes

Strengths: Comments about the paper: This paper presents convergence analysis of primal-dual natural policy gradient methods under the CMDP framework. Several recent works have shown convergence of policy gradients and optimality bounds (e.g Agarwal et al., Mei et al), but the paper extends similar analysis to (a) natural policy gradients (b) CMDP framework with constraints. Overall, it archives a sublinear rate of convergence in the CMDP framework, similar to other related works with convergence analysis. The analysis of the paper is done for the general MDP case with function approximation and restricted policy classes. It is a very well written paper that is easy to follow with significant theoretical derivation and proof details.