Articles liés à Mathematical Foundations of Reinforcement Learning
Synopsis
This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.
The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods.
With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Shiyu Zhao is currently an Associate Professor and Director of the Intelligent Unmanned Systems Laboratory in the School of Engineering at Westlake University, Hangzhou, China. He received his Ph.D. degree in Electrical and Computer Engineering from the National University of Singapore in 2014. Before joining Westlake University in 2019, he was a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield, UK. His primary research interest lies in decision-making and sensing of multi-robot systems.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Mathematical Foundation of Reinforcement Learning
ISBN 10 : 9819739438
ISBN 13 : 9789819739431
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a mathematical but friendly introduction to the fundamental concepts, basic problems, and classic algorithms in reinforcement learning. It can help readers understand the theoretical roots of an algorithm and hence why the algorithm wa. N° de réf. du vendeur 1658774905
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Mathematical Foundations of Reinforcement Learning
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Springer Nature Singapore Feb 2025, 2025
ISBN 10 : 9819739438
ISBN 13 : 9789819739431
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods.With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning. Englisch. N° de réf. du vendeur 9789819739431
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Mathematical Foundations of Reinforcement Learning
Edité par
Springer Nature Singapore, Springer Nature Singapore Jan 2025, 2025
ISBN 10 : 9819739438
ISBN 13 : 9789819739431
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch. N° de réf. du vendeur 9789819739431
Quantité disponible : 2 disponible(s)
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Mathematical Foundations of Reinforcement Learning
Edité par
Springer Nature Singapore, Springer Nature Singapore, 2025
ISBN 10 : 9819739438
ISBN 13 : 9789819739431
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability.The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods.With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning. N° de réf. du vendeur 9789819739431
Quantité disponible : 1 disponible(s)
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Mathematical Foundations of Reinforcement Learning
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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hardcover. Etat : New. New. book. N° de réf. du vendeur ERICA82998197394386
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