The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. Many methods derived from TSP have been applied to study autonomous vehicle route planning with fuel constraints. Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. This paper employs RL to solve the traveling salesman problem With refueling (TSPWR). The technique proposes a model (actions, states, reinforcements) and RL-TSPWR algorithm. Focus is given on the analysis of RL parameters and on the refueling influence in route learning optimization of fuel cost. Two RL algorithms: Q-learning and SARSA are compared. In addition, RL parameter estimation is performed by Response Surface Methodology, Analysis of Variance and Tukey Test. The proposed method achieves the best solution in 15 out of 16 case studies.

旅行商问题 (TSP) 是最著名的组合优化问题之一。许多源自 TSP 的方法已应用于研究具有燃料限制的自主车辆路线规划。然而，作为解决加油问题的潜在方法，强化学习（RL）很少受到关注。本文采用 RL 来解决旅行商加油问题（TSPWR）。该技术提出了一个模型（动作、状态、强化）和 RL-TSPWR 算法。重点分析了 RL 参数和加油对燃料成本路线学习优化的影响。比较了两种 RL 算法：Q-learning 和 SARSA。此外，RL 参数估计是通过响应面方法学、方差分析和 Tukey 检验来执行的。