Ottoni, André L. C. and Nepomuceno, Erivelton and Oliveira, Marcos S. de and Oliveira, Daniela C. R. de (2022) Reinforcement learning for the traveling salesman problem with refueling. Complex & Intelligent Systems, 8 (3). pp. 2001-2015. ISSN 2199-4536

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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.

Item Type:	Article
Keywords:	Reinforcement learning; Traveling salesman with refueling problem; Tuning of parameters;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	16817
Identification Number:	https://doi.org/10.1007/s40747-021-00444-4
Depositing User:	Erivelton Nepomuceno
Date Deposited:	09 Jan 2023 12:33
Journal or Publication Title:	Complex & Intelligent Systems
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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