MURAL - Maynooth University Research Archive Library

    Reinforcement learning for the traveling salesman problem with refueling

    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

    Download (765kB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    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:
    Depositing User: Erivelton Nepomuceno
    Date Deposited: 09 Jan 2023 12:33
    Journal or Publication Title: Complex & Intelligent Systems
    Publisher: Springer
    Refereed: Yes
    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

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads