书目名称 | Handbook of Reinforcement Learning and Control |
编辑 | Kyriakos G. Vamvoudakis,Yan Wan,Derya Cansever |
视频video | |
概述 | Enriches understanding of the applications of reinforcement learning for control of dynamic systems.Collates research from a wide-range of experts, creating a comprehensive guide.Discusses both theore |
丛书名称 | Studies in Systems, Decision and Control |
图书封面 |  |
描述 | .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. . |
出版日期 | Book 2021 |
关键词 | Reinforcement Learning; Privacy and Security; Control Systems; Cyber-Physical Systems; Distributed Contr |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-030-60990-0 |
isbn_softcover | 978-3-030-60992-4 |
isbn_ebook | 978-3-030-60990-0Series ISSN 2198-4182 Series E-ISSN 2198-4190 |
issn_series | 2198-4182 |
copyright | Springer Nature Switzerland AG 2021 |