NeurIPS 2020
### Learning Graph Structure With A Finite-State Automaton Layer

### Meta Review

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. Please integrate your responses in the rebuttal with the main paper. And naturally, consult the reviews for more detailed feedback.