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Mathematical foundations of reinforcement learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical foundations of reinforcement learning/ by Shiyu Zhao.
Author:	Zhao, Shiyu.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xvi, 275 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1007/978-981-97-3944-8
ISBN:	9789819739448

Mathematical foundations of reinforcement learning
Zhao, Shiyu.

Mathematical foundations of reinforcement learning[electronic resource] /by Shiyu Zhao. - Singapore :Springer Nature Singapore :2025. - xvi, 275 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789819739448

Standard No.: 10.1007/978-981-97-3944-8doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Mathematical foundations of reinforcement learning
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245 1 0 $a Mathematical foundations of reinforcement learning $h [electronic resource] / $c by Shiyu Zhao.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xvi, 275 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Reinforcement learning. $3 1006373
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Multiagent Systems. $3 3411992
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950 $a Computer Science (SpringerNature-11645)