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.

利用熵正则化最优传输技术作为深度强化学习网络中的一层，以实现更快速的学习并在端到端训练期间强制执行分配约束和规定，从而对于解决组合优化问题的效率进行优化。