Contribution to the study and the design of reinforcement functions

Autores
Santos, Juan Miguel
Año de publicación
2000
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The underlying concept in Reinforcement Learning is as simple as it is attractive: to learn by trial and error from the interaction with the environment. This approach allows us to deal with problems where a learning technique searches to improve the performance of the agent (the learner) over time. Reinforcement Learning groups a set of such techniques, and it uses a performance measure based on two types of signals given by a Critic or Reinforcement Function: penalty and reward.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Reinforcement Learning
Artificial Neural Networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/135464
Acceder
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spelling Contribution to the study and the design of reinforcement functionsSantos, Juan MiguelCiencias InformáticasReinforcement LearningArtificial Neural NetworksThe underlying concept in Reinforcement Learning is as simple as it is attractive: to learn by trial and error from the interaction with the environment. This approach allows us to deal with problems where a learning technique searches to improve the performance of the agent (the learner) over time. Reinforcement Learning groups a set of such techniques, and it uses a performance measure based on two types of signals given by a Critic or Reinforcement Function: penalty and reward.Sociedad Argentina de Informática e Investigación Operativa2000-06-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/135464spainfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/127info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:25:51Zoai:sedici.unlp.edu.ar:10915/135464Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:25:51.742SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Contribution to the study and the design of reinforcement functions
title Contribution to the study and the design of reinforcement functions
spellingShingle Contribution to the study and the design of reinforcement functions
Santos, Juan Miguel
Ciencias Informáticas
Reinforcement Learning
Artificial Neural Networks
title_short Contribution to the study and the design of reinforcement functions
title_full Contribution to the study and the design of reinforcement functions
title_fullStr Contribution to the study and the design of reinforcement functions
title_full_unstemmed Contribution to the study and the design of reinforcement functions
title_sort Contribution to the study and the design of reinforcement functions
dc.creator.none.fl_str_mv Santos, Juan Miguel
author Santos, Juan Miguel
author_facet Santos, Juan Miguel
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Reinforcement Learning
Artificial Neural Networks
topic Ciencias Informáticas
Reinforcement Learning
Artificial Neural Networks
dc.description.none.fl_txt_mv The underlying concept in Reinforcement Learning is as simple as it is attractive: to learn by trial and error from the interaction with the environment. This approach allows us to deal with problems where a learning technique searches to improve the performance of the agent (the learner) over time. Reinforcement Learning groups a set of such techniques, and it uses a performance measure based on two types of signals given by a Critic or Reinforcement Function: penalty and reward.
Sociedad Argentina de Informática e Investigación Operativa
description The underlying concept in Reinforcement Learning is as simple as it is attractive: to learn by trial and error from the interaction with the environment. This approach allows us to deal with problems where a learning technique searches to improve the performance of the agent (the learner) over time. Reinforcement Learning groups a set of such techniques, and it uses a performance measure based on two types of signals given by a Critic or Reinforcement Function: penalty and reward.
publishDate 2000
dc.date.none.fl_str_mv 2000-06-26
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/135464
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dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/127
info:eu-repo/semantics/altIdentifier/issn/1514-6774
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
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repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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