9783319783833 - reinforcement learning for optimal feedback control: a lyapunov-based approach (communications and control engineering) von kamalapurkar, rushikesh; walters, patrick; rosenfeld, joel; dixon, warren (11 Ergebnisse)
Reinforcement Learning for Optimal Feedback Contro
Kamalapurkar, Rushikesh; Walters, Patrick; Rosenfeld, Joel; Dixon, Warren
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
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Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach
Kamalapurkar, Rushikesh; Walters, Patrick; Rosenfeld, Joel; Dixon, Warren
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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-d…riven 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 disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
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Serie: Buch 46 von 65 - Communications and Control Engineering
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 unc…ertainty, 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 disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry. 312 pp. Englisch.
Reinforcement Learning for Optimal Feedback Control
Rushikesh Kamalapurkar|Patrick Walters|Joel Rosenfeld|Warren Dixon
Sprache: Englisch
Verlag: Springer International Publishing, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
- Print-on-Demand
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Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Illustrates the effectiveness of the developed methods with comparative simulations to leading off-line numerical methodsPresents theoretical development through engineering examples and hardware imple…mentations.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
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Buch. Zustand: Neu. Reinforcement Learning for Optimal Feedback Control | A Lyapunov-Based Approach | Rushikesh Kamalapurkar (u. a.) | Buch | Communications and Control Engineering | xvi | Englisch | 2018 | Springer | EAN 9783319783833 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg…, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Sprache: Englisch
Verlag: Springer International Publishing, Palgrave Macmillan Mai 2018, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 uncerta…inty, 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 disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 312 pp. Englisch.
Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach
Kamalapurkar, Rushikesh; Walters, Patrick; Rosenfeld, Joel; Dixon, Warren
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
- Print-on-Demand
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Reinforcement Learning for Optimal Feedback Control : A Lyapunov-Based Approach
Kamalapurkar, Rushikesh; Walters, Patrick; Rosenfeld, Joel; Dixon, Warren
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 46 von 65 - Communications and Control Engineering
- Hardcover
- Print-on-Demand
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