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...
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| Materyal Türü: | Online |
| Dil: | İngilizce |
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KIT Scientific Publishing
2021
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| Online Erişim: | 35389 |
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| _version_ | 1869524441201901568 |
|---|---|
| author | Deisenroth, Marc Peter |
| author_browse | Deisenroth, Marc Peter |
| author_facet | Deisenroth, Marc Peter |
| author_sort | Deisenroth, Marc Peter |
| collection | Directory of Open Access Books |
| 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. |
| format | Online |
| id | doab-20.500.12854ir-45907 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-459072023-12-20T18:40:48Z Efficient Reinforcement Learning using Gaussian Processes Deisenroth, Marc Peter QA75.5-76.95 autonomous learning Gaussian processes control machine learning Bayesian inference bic Book Industry Communication::U Computing & information technology::UY Computer science 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. 2021-02-11T12:10:46Z 2021-02-11T12:10:46Z 2019-07-30 20:02:01 2010 book 35389 18673813 9783866445697 https://directory.doabooks.org/handle/20.500.12854/45907 eng Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://www.ksp.kit.edu/9783866445697 KIT Scientific Publishing 10.5445/KSP/1000019799 10.5445/KSP/1000019799 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783866445697 IX, 205 p. open access |
| spellingShingle | QA75.5-76.95 autonomous learning Gaussian processes control machine learning Bayesian inference bic Book Industry Communication::U Computing & information technology::UY Computer science Deisenroth, Marc Peter Efficient Reinforcement Learning using Gaussian Processes |
| title | 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_short | Efficient Reinforcement Learning using Gaussian Processes |
| title_sort | efficient reinforcement learning using gaussian processes |
| topic | QA75.5-76.95 autonomous learning Gaussian processes control machine learning Bayesian inference bic Book Industry Communication::U Computing & information technology::UY Computer science |
| topic_facet | QA75.5-76.95 autonomous learning Gaussian processes control machine learning Bayesian inference bic Book Industry Communication::U Computing & information technology::UY Computer science |
| url | 35389 |
| work_keys_str_mv | AT deisenrothmarcpeter efficientreinforcementlearningusinggaussianprocesses |