Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods

arXiv:2404.16721v1 Announce Type: cross
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