  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.
