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Titlebook: Reinforcement Learning for Optimal Feedback Control; A Lyapunov-Based App Rushikesh Kamalapurkar,Patrick Walters,Warren Dixo Book 2018 Spri

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發(fā)表于 2025-3-21 17:35:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Reinforcement Learning for Optimal Feedback Control
副標(biāo)題A Lyapunov-Based App
編輯Rushikesh Kamalapurkar,Patrick Walters,Warren Dixo
視頻videohttp://file.papertrans.cn/826/825940/825940.mp4
概述Illustrates the effectiveness of the developed methods with comparative simulations to leading off-line numerical methods.Presents theoretical development through engineering examples and hardware imp
叢書名稱Communications and Control Engineering
圖書封面Titlebook: Reinforcement Learning for Optimal Feedback Control; A Lyapunov-Based App Rushikesh Kamalapurkar,Patrick Walters,Warren Dixo Book 2018 Spri
描述.Reinforcement Learning for Optimal Feedback Control .develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book’s focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. .To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor–critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements...This monograph provides academic researchers with backgrounds in diverse discipline
出版日期Book 2018
關(guān)鍵詞Nonlinear Control; Lyapunov-based Control; Reinforcement Learning; Optimal Control; Dynamic Programming;
版次1
doihttps://doi.org/10.1007/978-3-319-78384-0
isbn_softcover978-3-030-08689-3
isbn_ebook978-3-319-78384-0Series ISSN 0178-5354 Series E-ISSN 2197-7119
issn_series 0178-5354
copyrightSpringer International Publishing AG 2018
The information of publication is updating

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發(fā)表于 2025-3-21 22:09:28 | 只看該作者
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發(fā)表于 2025-3-22 01:10:19 | 只看該作者
Computational Considerations,strate the utility of the StaF methodology for the maintenance of accurate function approximation as well as solving an infinite horizon optimal regulation problem. The results of the simulation indicate that fewer basis functions are required to guarantee stability and approximate optimality than a
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發(fā)表于 2025-3-22 08:02:16 | 只看該作者
Book 2018 learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements...This monograph provides academic researchers with backgrounds in diverse discipline
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發(fā)表于 2025-3-22 11:03:11 | 只看該作者
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發(fā)表于 2025-3-22 14:45:04 | 只看該作者
Mix von wirtschaftsnahen wie zivilgesellschaftlichen Steuerungsmechanismen auszeichnen. Abschlie?end wird auf die ? Driver ? der ?konomisierung bei NPOs eingegangen und als Alternative hierzu eine Re-Orientierung in Richtung ? Mehr Zivilgesellschaft wagen! ? vorgeschlagen.
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發(fā)表于 2025-3-22 22:04:52 | 只看該作者
Rushikesh Kamalapurkar,Patrick Walters,Joel Rosenfeld,Warren Dixonrisch ges?ttigter Organisationstypen, dass sich Engagement und Engagementf?rderung in den Organisationen der Freien Wohlfahrtspflege überaus heterogen darstellen. Vor diesem Hintergrund wird der Frage nachgegangen, welche Formen des Managements beziehungsweise der Steuerung und Koordination sinnvoll
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發(fā)表于 2025-3-23 01:50:15 | 只看該作者
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發(fā)表于 2025-3-23 07:55:57 | 只看該作者
Rushikesh Kamalapurkar,Patrick Walters,Joel Rosenfeld,Warren Dixonndnissen als Selbstbeschreibung von Künstler*innen in T?tigkeiten der Offenen Settings vor. In diesen Selbstverst?ndnissen ordnen sich die Künstler*innen oft gerade nicht dem einen oder anderen Feld – Kunst oder Soziales – zu. Auch das ?differente Selbstverst?ndnis [von] 1 Von 2014 bis 2017 erarbeit
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