Items related to Control Systems and Reinforcement Learning
- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1316511960
- ISBN 13 9781316511961
- BindingHardcover
- LanguageEnglish
- Number of pages450
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Control Systems and Reinforcement Learning
Published by
Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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CONTROL SYSTEMS AND REINFORCEMENT LEARNING
Published by
Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
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Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
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Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
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Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
Published by
Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
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Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning
Published by
Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Hardcover
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Control Systems and Reinforcement Learning
Published by
Cambridge University Press, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Control Systems and Reinforcement Learning (Hardcover)
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Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960
ISBN 13: 9781316511961
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Hardcover
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
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Hardcover. Condition: new. Hardcover. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning. The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781316511961
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