Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods

This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement...

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Bibliographic Details
Published inarXiv.org
Main Authors Shin, Min Kyu, Su-Jeong, Park, Seung-Keol Ryu, Kim, Heeyeon, Han-Lim, Choi
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.04.2024
Subjects
Algorithms
Distillation
Machine learning
Supervised learning
Traveling salesman problem
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Summary:This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase trains an adaptation network to solve problems independently of privileged information. Before the first learning phase, a parameter initialization technique using the demonstration data was also devised to enhance training efficiency. The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and RL with demonstration schemes, most of which fail to sense all the task points.
ISSN:2331-8422