A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes...

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Detalles Bibliográficos
Autores: Carreras Pérez, Marc, Yuh, Junku, Batlle i Grabulosa, Joan, Ridao Rodríguez, Pere
Tipo de recurso: artículo
Fecha de publicación:2005
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/2169
Acceso en línea:http://hdl.handle.net/10256/2169
Access Level:acceso abierto
Palabra clave:Algorismes computacionals
Aprenentatge per reforç
Intel·ligència artificial
Robots autònoms
Xarxes neuronals (Informàtica)
Vehicles submergibles
Artificial intelligence
Autonomous robots
Computer algorithms
Neural networks (Computer science)
Reinforcement learning
Submersibles
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oai_identifier_str oai:recercat.cat:10256/2169
network_acronym_str ES
network_name_str España
repository_id_str
spelling A behavior-based scheme using reinforcement learning for autonomous underwater vehiclesCarreras Pérez, MarcYuh, JunkuBatlle i Grabulosa, JoanRidao Rodríguez, PereAlgorismes computacionalsAprenentatge per reforçIntel·ligència artificialRobots autònomsXarxes neuronals (Informàtica)Vehicles submergiblesArtificial intelligenceAutonomous robotsComputer algorithmsNeural networks (Computer science)Reinforcement learningSubmersiblesThis paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVsIEEE2005info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10256/2169http://hdl.handle.net/10256/2169© Oceanic Engineering, 2005, vol. 30, p. 416-427Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1109/JOE.2004.835805info:eu-repo/semantics/altIdentifier/issn/0364-9059Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10256/21692026-05-29T05:05:01Z
dc.title.none.fl_str_mv A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
title A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
spellingShingle A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
Carreras Pérez, Marc
Algorismes computacionals
Aprenentatge per reforç
Intel·ligència artificial
Robots autònoms
Xarxes neuronals (Informàtica)
Vehicles submergibles
Artificial intelligence
Autonomous robots
Computer algorithms
Neural networks (Computer science)
Reinforcement learning
Submersibles
title_short A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
title_full A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
title_fullStr A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
title_full_unstemmed A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
title_sort A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
dc.creator.none.fl_str_mv Carreras Pérez, Marc
Yuh, Junku
Batlle i Grabulosa, Joan
Ridao Rodríguez, Pere
author Carreras Pérez, Marc
author_facet Carreras Pérez, Marc
Yuh, Junku
Batlle i Grabulosa, Joan
Ridao Rodríguez, Pere
author_role author
author2 Yuh, Junku
Batlle i Grabulosa, Joan
Ridao Rodríguez, Pere
author2_role author
author
author
dc.subject.none.fl_str_mv Algorismes computacionals
Aprenentatge per reforç
Intel·ligència artificial
Robots autònoms
Xarxes neuronals (Informàtica)
Vehicles submergibles
Artificial intelligence
Autonomous robots
Computer algorithms
Neural networks (Computer science)
Reinforcement learning
Submersibles
topic Algorismes computacionals
Aprenentatge per reforç
Intel·ligència artificial
Robots autònoms
Xarxes neuronals (Informàtica)
Vehicles submergibles
Artificial intelligence
Autonomous robots
Computer algorithms
Neural networks (Computer science)
Reinforcement learning
Submersibles
description This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs
publishDate 2005
dc.date.none.fl_str_mv 2005
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/2169
http://hdl.handle.net/10256/2169
url http://hdl.handle.net/10256/2169
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/JOE.2004.835805
info:eu-repo/semantics/altIdentifier/issn/0364-9059
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv © Oceanic Engineering, 2005, vol. 30, p. 416-427
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411614512381952
score 15.812429

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