Handbook of Reinforcement Learning and Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Reinforcement Learning and Control write by Kyriakos G. Vamvoudakis. This book was released on 2021-06-23. Handbook of Reinforcement Learning and Control available in PDF, EPUB and Kindle. 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.
Handbook of Reinforcement Learning and Control
Handbook of Reinforcement Learning and Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Reinforcement Learning and Control write by Kyriakos G. Vamvoudakis. This book was released on 2021. Handbook of Reinforcement Learning and Control available in PDF, EPUB and Kindle. 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. .
Handbook of Learning and Approximate Dynamic Programming
Handbook of Learning and Approximate Dynamic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Learning and Approximate Dynamic Programming write by Jennie Si. This book was released on 2004-08-02. Handbook of Learning and Approximate Dynamic Programming available in PDF, EPUB and Kindle. A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors are leading researchers in the field
Reinforcement Learning
Reinforcement Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reinforcement Learning write by Jinna Li. This book was released on 2023-07-24. Reinforcement Learning available in PDF, EPUB and Kindle. 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-agent systems 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 usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
Reinforcement Learning, second edition
Reinforcement Learning, second edition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reinforcement Learning, second edition write by Richard S. Sutton. This book was released on 2018-11-13. Reinforcement Learning, second edition available in PDF, EPUB and Kindle. The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
