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Handbook of Reinforcement Learning and Control - Studies in Systems, Decision and Control 2021 edition
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Handbook of Reinforcement Learning and Control - Studies in Systems, Decision and Control
This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology.
The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including:
- deep learning;
- artificial intelligence;
- applications of game theory;
- mixed modality learning; and
- multi-agent reinforcement learning.
Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
833 pages, 145 Illustrations, color; 14 Illustrations, black and white; XXIV, 833 p. 159 illus., 145
| メディア | 書籍 Hardcover Book (ハードカバー付きの本) |
| リリース済み | 2021年6月24日 |
| ISBN13 | 9783030609894 |
| 出版社 | Springer Nature Switzerland AG |
| ページ数 | 833 |
| 寸法 | 150 × 220 × 20 mm · 1,37 kg |
| 言語 | ドイツ語 |
| 編集者 | Cansever, Derya |
| 編集者 | Lewis, Frank L. |
| 編集者 | Vamvoudakis, Kyriakos G. |
| 編集者 | Wan, Yan |