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.
@article{arxiv.2207.01443,
title = {Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning},
author = {Christian Löwens and Inaam Ashraf and Alexander Gembus and Genesis Cuizon and Jonas K. Falkner and Lars Schmidt-Thieme},
journal= {arXiv preprint arXiv:2207.01443},
year = {2022}
}
Comments
This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in KI 2022: Advances in Artificial Intelligence, and is available online at https://doi.org/10.1007/978-3-031-15791-2_14