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dc.creatorYanes Luis, Samueles
dc.creatorGutiérrez Reina, Danieles
dc.creatorToral, S. L.es
dc.date.accessioned2021-06-23T13:49:38Z
dc.date.available2021-06-23T13:49:38Z
dc.date.issued2021
dc.identifier.citationYanes Luis, S., Gutiérrez Reina, D. y Toral Marín, S. (2021). A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors. Sensors, 21 (8), Article number 2862.
dc.identifier.issn1424-8220es
dc.identifier.urihttps://hdl.handle.net/11441/114731
dc.descriptionArticle number 2862es
dc.description.abstractThe monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.es
dc.description.sponsorshipMinisterio de Ciencia, innovación y Universidades RTI2018-098964-B-I00es
dc.description.sponsorshipJunta de Andalucía US-1257508es
dc.description.sponsorshipJunta de Andalucía PY18-RE0009es
dc.description.sponsorshipJunta de Andalucía PAIDI TIC 201es
dc.formatapplication/pdfes
dc.format.extent30 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofSensors, 21 (8), Article number 2862.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectUnmanned Surface Vehicleses
dc.subjectEvolutionary Algorithmes
dc.subjectDeep Reinforcement Learninges
dc.subjectMachine learninges
dc.subjectIntelligent sensor systemes
dc.titleA Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensorses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Electrónicaes
dc.relation.projectIDRTI2018-098964-B-I00es
dc.relation.projectIDUS-1257508es
dc.relation.projectIDPY18-RE0009es
dc.relation.projectIDPAIDI TIC 201es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/8/2862es
dc.identifier.doi10.3390/s21082862es
dc.journaltitleSensorses
dc.publication.volumen21es
dc.publication.issue8es
dc.publication.initialPageArticle number 2862es

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