Ottoni, André L. C. and Nepomuceno, Erivelton and de Oliveira, Marcos S. and de Oliveira, Daniela C. R. (2020) Tuning of reinforcement learning parameters applied to SOP using the Scott–Knott method. Soft Computing, 24 (6). pp. 4441-4453. ISSN 1432-7643

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Abstract

In this paper, we present a technique to tune the reinforcement learning (RL) parameters applied to the sequential ordering problem (SOP) using the Scott–Knott method. The RL has been widely recognized as a powerful tool for combinatorial optimization problems, such as travelling salesman and multidimensional knapsack problems. It seems, however, that less attention has been paid to solve the SOP. Here, we have developed a RL structure to solve the SOP that can partially fill that gap. Two traditional RL algorithms, Q-learning and SARSA, have been employed. Three learning specifications have been adopted to analyze the performance of the RL: algorithm type, reinforcement learning function, and € parameter. A complete factorial experiment and the Scott–Knott method are used to find the best combination of factor levels, when the source of variation is statistically different in analysis of variance. The performance of the proposed RL has been tested using benchmarks from the TSPLIB library. In general, the selected parameters indicate that SARSA overwhelms the performance of Q-learning.

Item Type:	Article
Keywords:	Reinforcement learning; Sequential Ordering Problem; Factorial design; Scott–Knott method; Tuning parameters;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	16723
Identification Number:	https://doi.org/10.1007/s00500-019-04206-w
Depositing User:	Erivelton Nepomuceno
Date Deposited:	21 Nov 2022 15:20
Journal or Publication Title:	Soft Computing
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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