Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning

[EN] In the field of algorithm optimisation, finding the optimal configuration of parameters is essential for achieving peak performance. Traditional methods usually rely on manual tuning or exhaustive search techniques, which can be time-consuming and inefficient. This paper proposes a novel approa...

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Autores: Serrano-Ruiz, Julio Cesar|||0000-0002-6671-7077, Mula, Josefa|||0000-0002-8447-3387, Poler, R.|||0000-0003-4475-6371
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::950c64e1f8ef17d701c90c1f92663560
Acceso en línea:https://riunet.upv.es/handle/10251/235158
Access Level:acceso abierto
Palabra clave:Parameters tuning
Deep reinforcement learning
Holt-Winters
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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spelling Smart Tuning of Algorithm Parameters by Deep Reinforcement LearningSerrano-Ruiz, Julio Cesar|||0000-0002-6671-7077Mula, Josefa|||0000-0002-8447-3387Poler, R.|||0000-0003-4475-6371Parameters tuningDeep reinforcement learningHolt-Winters09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] In the field of algorithm optimisation, finding the optimal configuration of parameters is essential for achieving peak performance. Traditional methods usually rely on manual tuning or exhaustive search techniques, which can be time-consuming and inefficient. This paper proposes a novel approach utilising deep reinforcement learning (DRL) for automatically tuning the parameters of those algorithms that require it. By formulating the parameter tuning problem of the Holt-Winters algorithm as a reinforcement learning task, this research provides an example of the use of this method, which enables algorithms to autonomously tune their parameters based on feedback from the environment shaped by the problem. Leveraging an advantage actor-critic algorithm (A2C), our framework learns optimal parameter settings through exploration and exploitation strategies. The main findings highlight the versatility and efficiency of this DRL-based method for parameter tuning in optimising algorithms, paving the way for more adaptive and self-improving systems in various domains.The research leading to these results received funding from the EU Horizon Europe Programme with grant agreement No. 101057294 AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability and Resilience (AIDEAS), from the Valencian Regional Government with Ref. PROMETEO/2021/065 "Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT), and from the MCIN/AEI /https://doi.org/10.13039/501 100011033 with Ref. PDC2022 133957-I00 Validation of transferable results of optimisation of zero-defect enabling production technologies for Supply Chain 4.0 (CADS4.0-II).SpringerDepartamento de Organización de EmpresasCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Técnica Superior de Ingeniería IndustrialEscuela Politécnica Superior de AlcoyGENERALITAT VALENCIANAAGENCIA ESTATAL DE INVESTIGACIONCOMISION DE LAS COMUNIDADES EUROPEARepositorio Institucional de la Universitat Politècnica de València Riunet20252025-05-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/235158reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PDC2022-133957-I00 VALIDACION DE RESULTADOS TRANSFERIBLES DE OPTIMIZACION DE TECNOLOGIAS DE PRODUCCION CERO-DEFECTOS HABILITADORAS PARA CADENAS DE SUMINISTRO 4.0European Commission https://doi.org/10.13039/501100000780 HE 101057294 AI Driven industrial Equipment product life cycle boosting Agility, Sustainability and resilienceGENERALITAT VALENCIANA GENERALITAT VALENCIANA PROMETEO%2F2021%2F065 Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT)open accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::950c64e1f8ef17d701c90c1f926635602026-06-13T07:49:27Z
dc.title.none.fl_str_mv Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
title Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
spellingShingle Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
Serrano-Ruiz, Julio Cesar|||0000-0002-6671-7077
Parameters tuning
Deep reinforcement learning
Holt-Winters
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
title_full Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
title_fullStr Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
title_full_unstemmed Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
title_sort Smart Tuning of Algorithm Parameters by Deep Reinforcement Learning
dc.creator.none.fl_str_mv Serrano-Ruiz, Julio Cesar|||0000-0002-6671-7077
Mula, Josefa|||0000-0002-8447-3387
Poler, R.|||0000-0003-4475-6371
author Serrano-Ruiz, Julio Cesar|||0000-0002-6671-7077
author_facet Serrano-Ruiz, Julio Cesar|||0000-0002-6671-7077
Mula, Josefa|||0000-0002-8447-3387
Poler, R.|||0000-0003-4475-6371
author_role author
author2 Mula, Josefa|||0000-0002-8447-3387
Poler, R.|||0000-0003-4475-6371
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Organización de Empresas
Centro de Investigación en Gestión e Ingeniería de Producción
Escuela Técnica Superior de Ingeniería Industrial
Escuela Politécnica Superior de Alcoy
GENERALITAT VALENCIANA
AGENCIA ESTATAL DE INVESTIGACION
COMISION DE LAS COMUNIDADES EUROPEA
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Parameters tuning
Deep reinforcement learning
Holt-Winters
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Parameters tuning
Deep reinforcement learning
Holt-Winters
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] In the field of algorithm optimisation, finding the optimal configuration of parameters is essential for achieving peak performance. Traditional methods usually rely on manual tuning or exhaustive search techniques, which can be time-consuming and inefficient. This paper proposes a novel approach utilising deep reinforcement learning (DRL) for automatically tuning the parameters of those algorithms that require it. By formulating the parameter tuning problem of the Holt-Winters algorithm as a reinforcement learning task, this research provides an example of the use of this method, which enables algorithms to autonomously tune their parameters based on feedback from the environment shaped by the problem. Leveraging an advantage actor-critic algorithm (A2C), our framework learns optimal parameter settings through exploration and exploitation strategies. The main findings highlight the versatility and efficiency of this DRL-based method for parameter tuning in optimising algorithms, paving the way for more adaptive and self-improving systems in various domains.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-05-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/235158
url https://riunet.upv.es/handle/10251/235158
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PDC2022-133957-I00 VALIDACION DE RESULTADOS TRANSFERIBLES DE OPTIMIZACION DE TECNOLOGIAS DE PRODUCCION CERO-DEFECTOS HABILITADORAS PARA CADENAS DE SUMINISTRO 4.0
European Commission https://doi.org/10.13039/501100000780 HE 101057294 AI Driven industrial Equipment product life cycle boosting Agility, Sustainability and resilience
GENERALITAT VALENCIANA GENERALITAT VALENCIANA PROMETEO%2F2021%2F065 Industrial Production and Logistics Optimization in Industry 4.0 (i4OPT)
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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