  The traveling salesman problem is a fundamental combinatorial optimization
problem with strong exact algorithms. However, as problems scale up, these
exact algorithms fail to provide a solution in a reasonable time. To resolve
this, current works look at utilizing deep learning to construct reasonable
solutions. Such efforts have been very successful, but tend to be slow and
compute intensive. This paper exemplifies the integration of entropic
regularized optimal transport techniques as a layer in a deep reinforcement
learning network. We show that we can construct a model capable of learning
without supervision and inferences significantly faster than current
autoregressive approaches. We also empirically evaluate the benefits of
including optimal transport algorithms within deep learning models to enforce
assignment constraints during end-to-end training.
