  While there are optimal TSP solvers, as well as recent learning-based
approaches, the generalization of the TSP to the Multiple Traveling Salesmen
Problem is much less studied. Here, we design a neural network solution that
treats the salesmen, cities and depot as three different sets of varying
cardinalities. We apply a novel technique that combines elements from recent
architectures that were developed for sets, as well as elements from graph
networks. Coupled with new constraint enforcing output layers, a dedicated
loss, and a search method, our solution is shown to outperform all the
meta-heuristics of the leading solver in the field.
