Review for NeurIPS paper: Learning Graph Structure With A Finite-State Automaton Layer

In graph nets, edges can represent two kinds of relations: ones that follow immediately from the structure of the graph, and ones that are abstract/implicit. The paper proposes to learn the latter. More precisely, it considers relations defined as paths in the base graph accepted by a finite-state automaton, poses the problem of learning these relations as a POMDP problem, and solves a relaxed version of this problem using gradient descent. Overall, the paper was well-received. Pros: Fresh idea Clean formulation Experiments show clear gains in the domains considered The paper is well-written Cons: - Some missing related work - Somewhat narrow application domain The reviewers appreciated the clarifications provided in the author response, in particular the RL experiment for the "Go for a Walk" domain.