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Titlebook: Reinforcement Learning; Optimal Feedback Con Jinna Li,Frank L. Lewis,Jialu Fan Book 2023 The Editor(s) (if applicable) and The Author(s), u

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樓主
發(fā)表于 2025-3-21 19:19:27 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Reinforcement Learning
副標(biāo)題Optimal Feedback Con
編輯Jinna Li,Frank L. Lewis,Jialu Fan
視頻videohttp://file.papertrans.cn/826/825927/825927.mp4
概述Systematic, easy-to-follow introduction of novel ideas in data-driven optimal control.Uses measured data in examples to show how methods really work.Illustrates the practical application of novel algo
叢書名稱Advances in Industrial Control
圖書封面Titlebook: Reinforcement Learning; Optimal Feedback Con Jinna Li,Frank L. Lewis,Jialu Fan Book 2023 The Editor(s) (if applicable) and The Author(s), u
描述.This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems...?..A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agentsystems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic
出版日期Book 2023
關(guān)鍵詞Reinforcement Learning for Optimal Control; Process Engineering; Adaptive Dynamic Programming; Data-dri
版次1
doihttps://doi.org/10.1007/978-3-031-28394-9
isbn_softcover978-3-031-28396-3
isbn_ebook978-3-031-28394-9Series ISSN 1430-9491 Series E-ISSN 2193-1577
issn_series 1430-9491
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Reinforcement Learning影響因子(影響力)




書目名稱Reinforcement Learning影響因子(影響力)學(xué)科排名




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書目名稱Reinforcement Learning網(wǎng)絡(luò)公開度學(xué)科排名




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沙發(fā)
發(fā)表于 2025-3-21 22:16:26 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:45:45 | 只看該作者
Jinna Li,Frank L. Lewis,Jialu FanSystematic, easy-to-follow introduction of novel ideas in data-driven optimal control.Uses measured data in examples to show how methods really work.Illustrates the practical application of novel algo
地板
發(fā)表于 2025-3-22 05:54:42 | 只看該作者
5#
發(fā)表于 2025-3-22 11:44:06 | 只看該作者
6#
發(fā)表于 2025-3-22 14:15:20 | 只看該作者
Background on Reinforcement Learning and Optimal Control,ion and contributions of this book. The discussion is preparatory to well handle optimal feedback control problems using the RL technique in subsequent chapters, with strong potentials and benefits for future practical applications, particularly industrial intelligent optimization and control. In ad
7#
發(fā)表于 2025-3-22 19:48:24 | 只看該作者
Control Using Reinforcement Learning,er systems with a single source of external disturbances. The primary contribution lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than the on-policy learning. Then, we present a data-driven adaptive dynamic prog
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發(fā)表于 2025-3-23 00:22:54 | 只看該作者
9#
發(fā)表于 2025-3-23 03:18:57 | 只看該作者
Interleaved Robust Reinforcement Learning,terleaved reinforcement learning algorithm is developed for finding a robust controller of DT affine nonlinear systems subject to matched or unmatched uncertainties. To this end, the robust control problem is converted to the optimal control?problem for nominal systems by selecting an appropriate ut
10#
發(fā)表于 2025-3-23 09:09:14 | 只看該作者
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