東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Integral and inverse reinforcement learning for optimal control systems and games

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integral and inverse reinforcement learning for optimal control systems and games/ by Bosen Lian ... [et al.].
其他作者:	Lian, Bosen.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xx, 267 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-3-031-45252-9
ISBN:	9783031452529

Integral and inverse reinforcement learning for optimal control systems and games
Integral and inverse reinforcement learning for optimal control systems and games[electronic resource] /by Bosen Lian ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xx, 267 p. :ill. (chiefly col.), digital ;24 cm. - Advances in industrial control,2193-1577. - Advances in industrial control..

1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.

Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains - aircraft, robotics, power systems, and communication networks among them - with theoretical insights valuable in tackling the real-world challenges they face.

ISBN: 9783031452529

Standard No.: 10.1007/978-3-031-45252-9doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Integral and inverse reinforcement learning for optimal control systems and games
LDR:02883nmm a22003495a 4500 001 2370378
003 DE-He213
005 20240319222149.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031452529 $q (electronic bk.)
020 $a 9783031452512 $q (paper)
024 7 $a 10.1007/978-3-031-45252-9 $2 doi
035 $a 978-3-031-45252-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6
072 7 $a TJFM $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC007000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .I61 2024
245 0 0 $a Integral and inverse reinforcement learning for optimal control systems and games $h [electronic resource] / $c by Bosen Lian ... [et al.].
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xx, 267 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Advances in industrial control, $x 2193-1577
505 0 $a 1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.
520 $a Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains - aircraft, robotics, power systems, and communication networks among them - with theoretical insights valuable in tackling the real-world challenges they face.
650 0 $a Reinforcement learning. $3 1006373
650 1 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Mathematical and Computational Engineering Applications. $3 3592737
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Automotive Engineering. $3 928032
700 1 $a Lian, Bosen. $3 3715682
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in industrial control. $3 1566025
856 4 0 $u https://doi.org/10.1007/978-3-031-45252-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)