國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Reinforcement learning for optimal f...

Kamalapurkar, Rushikesh.

Reinforcement learning for optimal feedback controla Lyapunov-based approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning for optimal feedback controlby Rushikesh Kamalapurkar ... [et al.].
其他題名:	a Lyapunov-based approach /
其他作者:	Kamalapurkar, Rushikesh.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xvi, 293 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Reinforcement learning.
電子資源:	http://dx.doi.org/10.1007/978-3-319-78384-0
ISBN:	9783319783840$q(electronic bk.)

Reinforcement learning for optimal feedback controla Lyapunov-based approach /
Reinforcement learning for optimal feedback controla Lyapunov-based approach /[electronic resource] :by Rushikesh Kamalapurkar ... [et al.]. - Cham :Springer International Publishing :2018. - xvi, 293 p. :ill., digital ;24 cm. - Communications and control engineering,0178-5354. - Communications and control engineering..

Chapter 1. Optimal control -- Chapter 2. Approximate dynamic programming -- Chapter 3. Excitation-based online approximate optimal control -- Chapter 4. Model-based reinforcement learning for approximate optimal control -- Chapter 5. Differential Graphical Games -- Chapter 6. Applications -- Chapter 7. Computational considerations -- Reference -- Index.

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 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.

ISBN: 9783319783840$q(electronic bk.)

Standard No.: 10.1007/978-3-319-78384-0doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Reinforcement learning for optimal feedback controla Lyapunov-based approach /
LDR:02954nmm a2200325 a 4500 001 538814
003 DE-He213
005 20181122092829.0
006 m d
007 cr nn 008maaau
008 190122s2018 gw s 0 eng d
020 $a 9783319783840$q(electronic bk.)
020 $a 9783319783833$q(paper)
024 7 $a 10.1007/978-3-319-78384-0 $2 doi
035 $a 978-3-319-78384-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .R367 2018
245 0 0 $a Reinforcement learning for optimal feedback control $h [electronic resource] : $b a Lyapunov-based approach / $c by Rushikesh Kamalapurkar ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xvi, 293 p. : $b ill., digital ; $c 24 cm.
490 1 $a Communications and control engineering, $x 0178-5354
505 0 $a Chapter 1. Optimal control -- Chapter 2. Approximate dynamic programming -- Chapter 3. Excitation-based online approximate optimal control -- Chapter 4. Model-based reinforcement learning for approximate optimal control -- Chapter 5. Differential Graphical Games -- Chapter 6. Applications -- Chapter 7. Computational considerations -- Reference -- Index.
520 $a 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 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.
650 0 $a Reinforcement learning. $3 349131
650 0 $a Feedback control systems. $3 182018
650 1 4 $a Engineering. $3 210888
650 2 4 $a Control. $3 349080
650 2 4 $a Calculus of Variations and Optimal Control; Optimization. $3 274198
650 2 4 $a Systems Theory, Control. $3 274654
650 2 4 $a Communications Engineering, Networks. $3 273745
700 1 $a Kamalapurkar, Rushikesh. $3 816004
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Communications and control engineering. $3 558655
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-78384-0
950 $a Engineering (Springer-11647)