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Ottoni, André L. C., 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: | 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 |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/16742 |
| 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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