Moving in a Simulated Environment Through Deep Reinforcement Learning

Autores
Esarte, Javier; Folino, Pablo Daniel; Gómez, Juan Carlos
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.
UTN FRBA
Convocatoria Viajes y Eventos FRBA año 2022
Fil: Esarte, Javier. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.
Fil: Folino, Pablo Daniel. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.
Fil: Gómez, Juan Carlos. Instituto Nacional de Tecnología Industrial. Grupo de Inteligencia Artificial; Argentina.
Materia
deep reinforcement learning
soft actor-critic
tetrapod robot
virtual environment
predictive control
machine learning
robotics
artificial neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-07-23T14:52:28Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/11143
Acceder
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spelling Moving in a Simulated Environment Through Deep Reinforcement LearningEsarte, JavierFolino, Pablo DanielGómez, Juan Carlosdeep reinforcement learningsoft actor-critictetrapod robotvirtual environmentpredictive controlmachine learningroboticsartificial neural networksReinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.UTN FRBAConvocatoria Viajes y Eventos FRBA año 2022Fil: Esarte, Javier. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.Fil: Folino, Pablo Daniel. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.Fil: Gómez, Juan Carlos. Instituto Nacional de Tecnología Industrial. Grupo de Inteligencia Artificial; Argentina.IEEE2024-07-23T14:52:28Z2024-07-23T14:52:28Z2022-09-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciapdfapplication/pdfJ. Esarte, P. D. Folino and J. C. Gómez, "Moving in a Simulated Environment Through Deep Reinforcement Learning," 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina, 2022, pp. 1-6, doi: 10.1109/ARGENCON55245.2022.9939868.978-1-6654-8014-7978-1-6654-8015-4http://hdl.handle.net/20.500.12272/1114310.1109/ARGENCON55245.2022.9939868engenginfo:eu-repo/semantics/openAccess2024-07-23T14:52:28ZAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Atribución (“Creative Commons Attribution”): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). No comercial (“Creative Commons Non Commercial”): La explotación de la obra queda limitada a usos no comerciales. Sin obras derivadas (“Creative Commons No Derivate Works”): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.).reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:48:32Zoai:ria.utn.edu.ar:20.500.12272/11143instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:48:32.645Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Moving in a Simulated Environment Through Deep Reinforcement Learning
title Moving in a Simulated Environment Through Deep Reinforcement Learning
spellingShingle Moving in a Simulated Environment Through Deep Reinforcement Learning
Esarte, Javier
deep reinforcement learning
soft actor-critic
tetrapod robot
virtual environment
predictive control
machine learning
robotics
artificial neural networks
title_short Moving in a Simulated Environment Through Deep Reinforcement Learning
title_full Moving in a Simulated Environment Through Deep Reinforcement Learning
title_fullStr Moving in a Simulated Environment Through Deep Reinforcement Learning
title_full_unstemmed Moving in a Simulated Environment Through Deep Reinforcement Learning
title_sort Moving in a Simulated Environment Through Deep Reinforcement Learning
dc.creator.none.fl_str_mv Esarte, Javier
Folino, Pablo Daniel
Gómez, Juan Carlos
author Esarte, Javier
author_facet Esarte, Javier
Folino, Pablo Daniel
Gómez, Juan Carlos
author_role author
author2 Folino, Pablo Daniel
Gómez, Juan Carlos
author2_role author
author
dc.subject.none.fl_str_mv deep reinforcement learning
soft actor-critic
tetrapod robot
virtual environment
predictive control
machine learning
robotics
artificial neural networks
topic deep reinforcement learning
soft actor-critic
tetrapod robot
virtual environment
predictive control
machine learning
robotics
artificial neural networks
dc.description.none.fl_txt_mv Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.
UTN FRBA
Convocatoria Viajes y Eventos FRBA año 2022
Fil: Esarte, Javier. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.
Fil: Folino, Pablo Daniel. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Grupo de Inteligencia Artificial y Robótica; Argentina.
Fil: Gómez, Juan Carlos. Instituto Nacional de Tecnología Industrial. Grupo de Inteligencia Artificial; Argentina.
description Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-08
2024-07-23T14:52:28Z
2024-07-23T14:52:28Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv J. Esarte, P. D. Folino and J. C. Gómez, "Moving in a Simulated Environment Through Deep Reinforcement Learning," 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina, 2022, pp. 1-6, doi: 10.1109/ARGENCON55245.2022.9939868.
978-1-6654-8014-7
978-1-6654-8015-4
http://hdl.handle.net/20.500.12272/11143
10.1109/ARGENCON55245.2022.9939868
identifier_str_mv J. Esarte, P. D. Folino and J. C. Gómez, "Moving in a Simulated Environment Through Deep Reinforcement Learning," 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina, 2022, pp. 1-6, doi: 10.1109/ARGENCON55245.2022.9939868.
978-1-6654-8014-7
978-1-6654-8015-4
10.1109/ARGENCON55245.2022.9939868
url http://hdl.handle.net/20.500.12272/11143
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-07-23T14:52:28Z
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución (“Creative Commons Attribution”): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). No comercial (“Creative Commons Non Commercial”): La explotación de la obra queda limitada a usos no comerciales. Sin obras derivadas (“Creative Commons No Derivate Works”): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.).
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-07-23T14:52:28Z
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución (“Creative Commons Attribution”): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). No comercial (“Creative Commons Non Commercial”): La explotación de la obra queda limitada a usos no comerciales. Sin obras derivadas (“Creative Commons No Derivate Works”): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.).
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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