Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
<|reference_start|>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 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.<|reference_end|>
@article{shin2024distilling,
    title={Distilling Privileged Information for Dubins Traveling Salesman Problems
  with Neighborhoods},
    author={Min Kyu Shin, Su-Jeong Park, Seung-Keol Ryu, Heeyeon Kim and Han-Lim
  Choi},
    journal={arXiv preprint arXiv:2404.16721},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16721},
    primaryClass={cs.AI cs.LG}
}