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:	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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