Mathematical Foundations of Reinforcement Learning by Shiyu Zhao – Hardcover (ISBN: 9789819739431)

By Shiyu Zhao

Mathematical Foundations of Reinforcement Learning by Shiyu Zhao is a rigorous yet accessible textbook on the mathematical principles behind reinforcement learning. Published by Springer Singapore / Tsinghua University Press, this book introduces reinforcement learning through Markov decision processes, Bellman equations, Bellman optimality, value functions, policy evaluation, dynamic programming, temporal-difference learning, Q-learning, stochastic approximation, policy gradient methods, and function approximation.

Designed for computer science departments, artificial intelligence programs, machine learning courses, data science programs, robotics labs, mathematics departments, engineering schools, research institutes, university libraries, and institutional procurement teams, this textbook supports advanced study in reinforcement learning theory, sequential decision-making, stochastic processes, optimization, and algorithmic foundations of modern AI.

What This Book Does

This book helps readers understand why reinforcement learning algorithms work, not just how to implement them. It develops the mathematical foundation of reinforcement learning step by step, explaining value functions, Bellman operators, convergence ideas, optimal policies, model-based and model-free learning, stochastic approximation, temporal-difference methods, Q-learning, policy gradients, and approximation-based reinforcement learning.

Key Features

• Mathematical introduction to reinforcement learning from Springer Singapore / Tsinghua University Press
• Written by Shiyu Zhao
• Covers Markov decision processes, Bellman equations, Bellman optimality equations, value iteration, and policy iteration
• Explains stochastic approximation, temporal-difference learning, Q-learning, and policy gradient methods
• Supports rigorous study of reinforcement learning theory, convergence, optimization, and sequential decision-making
• Strong fit for AI, machine learning, computer science, robotics, mathematics, and engineering programs

Who Should Use This Book?

• Graduate and advanced undergraduate students in machine learning and artificial intelligence
• Computer science, data science, robotics, and engineering departments
• Faculty teaching reinforcement learning, sequential decision-making, and AI theory
• Researchers working on reinforcement learning algorithms, control, robotics, and optimization
• Mathematics and applied mathematics programs studying stochastic processes and dynamic programming
• University libraries, AI labs, and institutional research collections
• Institutions purchasing AI and machine learning textbooks in bulk for course adoption or library use

Why It’s Essential

• Builds a rigorous mathematical foundation for understanding modern reinforcement learning algorithms
• Connects Markov decision processes, Bellman equations, stochastic approximation, and optimization methods
• Helps students move beyond implementation toward theoretical understanding and algorithmic analysis
• Strong fit for AI research groups, machine learning courses, graduate seminars, engineering programs, and academic libraries

A rigorous reinforcement learning textbook for AI, machine learning, robotics, optimization, sequential decision-making, and mathematical foundations of modern artificial intelligence.
Order today from BooksGoat for reliable academic supply and institutional bulk purchasing.

Product Details

• ISBN-13: 9789819739431
• Author: Shiyu Zhao
• Publisher: Springer Singapore / Tsinghua University Press
• Format: Hardcover
• DOI: 10.1007/978-981-97-3944-8
• Publication Date: January 22, 2025
• Condition: New
• Price: $33.99
• Minimum Order: 5 Copies
• Availability: In Stock

TOC Highlights

• Introduction to Reinforcement Learning
• Markov Decision Processes
• State Values and Action Values
• Bellman Equation
• Bellman Optimality Equation
• Value Iteration and Policy Iteration
• Monte Carlo Methods
• Stochastic Approximation
• Temporal-Difference Learning
• Q-Learning and Model-Free Control
• Policy Gradient Methods
• Function Approximation in Reinforcement Learning

FAQs

• Who is the author of Mathematical Foundations of Reinforcement Learning?
  The book is written by Shiyu Zhao.
• Who publishes this book?
  The book is published by Springer Singapore / Tsinghua University Press.
• What does this book cover?
  It covers the mathematical foundations of reinforcement learning, including Markov decision processes, Bellman equations, Bellman optimality, value iteration, policy iteration, stochastic approximation, temporal-difference learning, Q-learning, policy gradients, and function approximation.
• Is this book suitable for machine learning courses?
  Yes. It is suitable for advanced machine learning, artificial intelligence, reinforcement learning, robotics, data science, computer science, engineering, and applied mathematics courses.
• Does this book focus on theory or implementation?
  The book focuses strongly on mathematical understanding and theoretical foundations while also explaining classical reinforcement learning algorithms and their structure.
• Is this suitable for institutional and bulk orders?
  Yes. BooksGoat supports bulk purchasing for universities, AI labs, computer science departments, engineering programs, research institutes, libraries, and institutional procurement teams.