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    A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem


    Ottoni, André L. C. and Nepomuceno, Erivelton and de Oliveira, Marcos S. (2018) A Response Surface Model Approach to Parameter Estimation of Reinforcement Learning for the Travelling Salesman Problem. Journal of Control, Automation and Electrical Systems, 29 (3). pp. 350-359. ISSN 2195-3880

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    Abstract

    This paper reports the use of response surface model (RSM) and reinforcement learning (RL) to solve the travelling salesman problem (TSP). In contrast to heuristically approaches to estimate the parameters of RL, the method proposed here allows a systematic estimation of the learning rate and the discount factor parameters.The Q-learning and SARSA algorithms were applied to standard problems from the TSPLIB library. Computational results demonstrate that the use of RSM is capable of producing better solutions to both symmetric and asymmetric tests of TSP.

    Item Type: Article
    Keywords: Reinforcement learning, Travelling salesman problem; Response surface model;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 16742
    Identification Number: https://doi.org/10.1007/s40313-018-0374-y
    Depositing User: Erivelton Nepomuceno
    Date Deposited: 22 Nov 2022 15:17
    Journal or Publication Title: Journal of Control, Automation and Electrical Systems
    Publisher: Springer
    Refereed: Yes
    URI:

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