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Mathematical Foundations of Reinforcement Learning

By Shiyu Zhao

Mathematical Foundations of Reinforcement Learning Computer Science

Mathematical Foundations of Reinforcement Learning

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Description
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.
Product details
Edition:
1
Number of Pages:
292
Release Date:
2025-01-22
Publication Date:
2025-01-22
Publisher:
Springer
Languages:
Original: English
ISBN10:
9819739438
ISBN13:
9789819739431
GPSR Manufacturer Reference:
[email protected]
Weight:
749 g
Height:
183 cm
Width:
260 cm
Thickness:
22 cm
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