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.

文中提出了一种新的学习方法，用于解决带有邻域的 Dubins 旅行推销员问题 (DTSPN)，以快速生成通过给定任务点邻域的非全向车辆的路径。该方法包括两个学习阶段：首先，模型无关的强化学习方法利用特权信息从 LinKernighan 启发式算法生成的专家轨迹中提取知识；随后，监督学习阶段训练一个适应网络，独立解决问题而不依赖特权信息。在第一个学习阶段之前，还设计了一种使用演示数据进行参数初始化的技术，以提高训练效率。所提出的学习方法比 LKH 快约 50 倍，显著优于其他模仿学习和演示式 RL 方法，其中大多数无法感知到所有任务点。