Abstract
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.
Highlights
The traveling salesman problem (TSP) is one of the bestknown combinatorial optimization problems and is often considered in autonomous vehicle route planning [11,19,31, 48,50,65,80]
We have proposed an Reinforcement learning (RL) structure to solve the traveling salesman problem with refueling (TSPWR), through a model and RL-traveling salesman problem With refueling (TSPWR) algorithm
The outline of the contributions of this paper relative to the recent literature in the field can be summarized as: (i) proposal for TSPWR formulation problem; (ii) algorithm for applying the RL to the TSPWR resolution; (iii) development of instances based on real data from the ANP; (iv) experiments realization under uniform and non-uniform cost conditions; (v) tuning of RL parameters applied to TSPWR using the statistical methods
Summary
The traveling salesman problem (TSP) is one of the bestknown combinatorial optimization problems and is often considered in autonomous vehicle route planning [11,19,31, 48,50,65,80]. The agent must visit each node (city) only once, considering equivalent the initial final position (goal) of route. An important research area for autonomous vehicle route planning considers fuel constraints [35,78]. In such cases, the challenge is to define a route to ensure that the vehicle carries out all the way without finishing the fuel. Following this same line, refueling problems seek to optimize the expenditure on the fuel purchase for road routes [27,60,71]
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