Review for NeurIPS paper: Learning Retrospective Knowledge with Reverse Reinforcement Learning

Strengths: 1) This paper focuses on an interesting and practical case of reinforcement learning. Clear examples are provided to demonstrate the difference between predictive knowledge (general RL) and retrospective knowledge (this work), how RL with retrospective knowledge can be used in real-world applications, and why general RL algorithms (GVFs) fail to represent such knowledge. The formulation is general so that multiple existing RL algorithms can be extended to the Reverse RL setting. Theoretical analysis is given to justify the convergence of Reverse RL algorithms (under linear function approximation).