Efficient Reinforcement Learning using Gaussian Processes
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2010
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/17578
- Acceso en línea:
- https://directory.doabooks.org/handle/20.500.12854/45907
http://hdl.handle.net/20.500.12010/17578
- Palabra clave:
- Autonomous learning
Gaussian processes
Machine learning
Aprendizaje
Aprendizaje experiencial
Aptitud de aprendizaje
- Rights
- License
- Abierto (Texto Completo)
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| dc.title.spa.fl_str_mv |
Efficient Reinforcement Learning using Gaussian Processes |
| title |
Efficient Reinforcement Learning using Gaussian Processes |
| spellingShingle |
Efficient Reinforcement Learning using Gaussian Processes Autonomous learning Gaussian processes Machine learning Aprendizaje Aprendizaje experiencial Aptitud de aprendizaje |
| title_short |
Efficient Reinforcement Learning using Gaussian Processes |
| title_full |
Efficient Reinforcement Learning using Gaussian Processes |
| title_fullStr |
Efficient Reinforcement Learning using Gaussian Processes |
| title_full_unstemmed |
Efficient Reinforcement Learning using Gaussian Processes |
| title_sort |
Efficient Reinforcement Learning using Gaussian Processes |
| dc.subject.spa.fl_str_mv |
Autonomous learning Gaussian processes Machine learning |
| topic |
Autonomous learning Gaussian processes Machine learning Aprendizaje Aprendizaje experiencial Aptitud de aprendizaje |
| dc.subject.lemb.spa.fl_str_mv |
Aprendizaje Aprendizaje experiencial Aptitud de aprendizaje |
| description |
This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems. |
| publishDate |
2010 |
| dc.date.created.none.fl_str_mv |
2010 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-22T17:35:19Z |
| dc.date.available.none.fl_str_mv |
2021-02-22T17:35:19Z |
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http://purl.org/coar/resource_type/c_2f33 |
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http://purl.org/coar/resource_type/c_2f33 |
| dc.identifier.isbn.none.fl_str_mv |
9783866445697 |
| dc.identifier.other.none.fl_str_mv |
https://directory.doabooks.org/handle/20.500.12854/45907 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12010/17578 |
| dc.identifier.doi.none.fl_str_mv |
10.5445/KSP/1000019799 |
| identifier_str_mv |
9783866445697 10.5445/KSP/1000019799 |
| url |
https://directory.doabooks.org/handle/20.500.12854/45907 http://hdl.handle.net/20.500.12010/17578 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.local.spa.fl_str_mv |
Abierto (Texto Completo) |
| dc.rights.creativecommons.none.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| rights_invalid_str_mv |
Abierto (Texto Completo) https://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.spa.fl_str_mv |
IX, 205 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
KIT Scientific Publishing |
| institution |
Universidad de Bogotá Jorge Tadeo Lozano |
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2021-02-22T17:35:19Z2021-02-22T17:35:19Z20109783866445697https://directory.doabooks.org/handle/20.500.12854/45907http://hdl.handle.net/20.500.12010/1757810.5445/KSP/1000019799This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.IX, 205 páginasapplication/pdfengKIT Scientific PublishingAutonomous learningGaussian processesMachine learningAprendizajeAprendizaje experiencialAptitud de aprendizajeEfficient Reinforcement Learning using Gaussian ProcessesAbierto (Texto Completo)https://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2http://purl.org/coar/resource_type/c_2f33Deisenroth, Marc PeterORIGINAL978-3-86644-569-7_pdfa.pdf978-3-86644-569-7_pdfa.pdfVer documentoapplication/pdf5639599https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17578/1/978-3-86644-569-7_pdfa.pdfc88f0ebdb408c3e832898eaffe127dc5MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17578/2/license.txtabceeb1c943c50d3343516f9dbfc110fMD52open accessTHUMBNAIL978-3-86644-569-7_pdfa.pdf.jpg978-3-86644-569-7_pdfa.pdf.jpgIM Thumbnailimage/jpeg11207https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17578/3/978-3-86644-569-7_pdfa.pdf.jpgd2ebd88c86586ec1931d8ddf1771cbf1MD53open access20.500.12010/17578oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/175782021-02-22 12:36:41.117open accessRepositorio Institucional - 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