论文标题
通过深入强化学习解决旅行销售人员问题，并通过优先限制
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
论文作者
论文摘要
这项工作通过调整适合常规TSP非常有效的最新方法，并使用深入的加固学习（DRL）提供了前往旅行销售人员问题的解决方案（TSPPC）。这些方法共有的是基于多头注意（MHA）层的图形模型的使用。解决拾取和交付问题（PDP）的一个想法是使用异质注意力嵌入每个节点可以扮演的不同可能的角色。在这项工作中，我们将这种异质注意的概念推广到TSPPC。此外，我们适应了最近的想法，以使注意力稀疏以获得更好的可扩展性。总体而言，我们通过应用和评估解决TSPPC的最新DRL方法为研究界做出了贡献。
This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that work well for regular TSPs. Common to these approaches is the use of graph models based on multi-head attention (MHA) layers. One idea for solving the pickup and delivery problem (PDP) is using heterogeneous attentions to embed the different possible roles each node can take. In this work, we generalize this concept of heterogeneous attentions to the TSPPC. Furthermore, we adapt recent ideas to sparsify attentions for better scalability. Overall, we contribute to the research community through the application and evaluation of recent DRL methods in solving the TSPPC.
