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Mathematical Foundation of Reinforcement Learning

Autor Shiyu Zhao
en Limba Engleză Hardback – 12 sep 2024
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 was designed in the first place and why it works. Many illustrative examples are given, and the mathematics is presented in a carefully designed manner to ensure that the book is friendly to read.
The contents of this book include two aspects. The first is about the mathematical foundation of reinforcement learning, which includes the Bellman equation, Bellman optimality equation, value iteration and policy iteration methods, and stochastic approximation. The second is about classic reinforcement learning algorithms, which include Monte Carlo methods, temporal-difference methods, function approximation, policy gradient, and actor-critic methods.
Given its scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning.
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Specificații

ISBN-13: 9789819739431
ISBN-10: 9819739438
Pagini: 300
Ilustrații: Approx. 300 p. 50 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția:2024
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore

Cuprins

1 Basic Concepts.- 2 State Value and Bellman Equation.- 3 Optimal State Value and Bellman Optimality Equation.- 4 Value Iteration and Policy Iteration.- 5 Monte Carlo Learning.- 6 Stochastic Approximation.- 7 Temporal-Difference Learning.- 8 Value Function Approximation.- 9 Policy Gradient.- 10 Actor-Critic Methods.

Notă biografică

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.
 

Textul de pe ultima copertă

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 was designed in the first place and why it works. Many illustrative examples are given, and the mathematics is presented in a carefully designed manner to ensure that the book is friendly to read.
The contents of this book include two aspects. The first is about the mathematical foundation of reinforcement learning, which includes the Bellman equation, Bellman optimality equation, value iteration and policy iteration methods, and stochastic approximation. The second is about classic reinforcement learning algorithms, which include Monte Carlo methods, temporal-difference methods, function approximation, policy gradient, and actor-critic methods.
Given its scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning.

Caracteristici

Offers a friendly mathematical intro to reinforcement learning for thorough and swift understanding Covers coherent and systematic content including fundamental concepts, basic problems, and classic algorithms Includes a wealth of examples to help illustrate the topics discussed