[2404.16721] Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods

Computer Science > Artificial Intelligence arXiv:2404.16721 (cs) This paper has been withdrawn by Min Kyu Shin [Submitted on 25 Apr 2024 (v1), last revised 5 Mar 2026 (this version, v2)] Title:Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods Authors:Min Kyu Shin, Su-Jeong Park, Seung-Keol Ryu, Heeyeon Kim, Han-Lim Choi View a PDF of the paper titled Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods, by Min Kyu Shin and 3 other authors No PDF available, click to view other formats 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. 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 f...