Name: Mathematical Foundations of Reinforcement Learning
SKU: 64795041
Price: 1611.00 ZAR
Availability: InStock
Author: Shiyu Zhao
ISBN: 9789819739431

portada Mathematical Foundations of Reinforcement Learning

Mathematical Foundations of Reinforcement Learning

Type

Physical Book

Publisher

Springer Nature

Author

Shiyu Zhao

Year

2025

Pages

275

Format

Hardcover

Dimensions

25.4x17.8 cm

ISBN13

9789819739431

Mathematical Foundations of Reinforcement Learning

Shiyu Zhao (Author) · Springer Nature · Hardcover

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Synopsis "Mathematical Foundations of Reinforcement Learning"

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.