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Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
April 26, 2024, 4:42 a.m. | Min Kyu Shin, Su-Jeong Park, Seung-Keol Ryu, Heeyeon Kim, Han-Lim Choi
cs.LG updates on arXiv.org arxiv.org
Abstract: 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. …
abstract arxiv cs.ai cs.lg free information novel paper reinforcement reinforcement learning through type
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