Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms

The book provides a theoretical foundation with elementary proofs of key theorems in decision-making under uncertainty, including reinforcement learning and learning with expert advice, and is aimed at graduate students interested in these topics.

Format: Hardback
Length: 243 pages
Publication date: 03 December 2022
Publisher: Springer International Publishing AG

This comprehensive book delves into the realm of decision-making under uncertainty, focusing on cutting-edge research areas such as reinforcement learning and learning with expert advice. It stands out as a unique resource, as it brings together the core elements of decision theory, Markov decision processes, and reinforcement learning in a concise and accessible format. The authors' primary objective was to establish a solid theoretical foundation, accompanied by elementary proofs of the most significant theorems in the field, all consolidated in a single volume. This book is particularly valuable for graduate students seeking to delve into statistical decision-making and the fundamental principles of reinforcement learning.

The book begins by introducing the fundamental concepts and principles of decision-making under uncertainty. It explores the challenges posed by uncertain environments and the need for efficient decision-making algorithms. The authors then delve into the theory of Markov decision processes, which provide a framework for modeling and analyzing decision-making scenarios. They discuss the principles of reinforcement learning, which involve learning from feedback and adapting behavior to achieve desired outcomes.

Throughout the book, the authors present recent research findings and applications in various domains, including robotics, finance, healthcare, and game theory. They discuss the advantages and limitations of different reinforcement learning algorithms, such as policy iteration, value iteration, and deep reinforcement learning, and illustrate their practical implications with real-world examples.

In addition to its theoretical content, the book includes numerous exercises and problems to help readers grasp the concepts and apply them to real-world scenarios. The solutions to these problems are provided, allowing readers to deepen their understanding and reinforce their learning.

This book is an essential resource for graduate students, researchers, and practitioners interested in statistical decision-making under uncertainty and reinforcement learning. It provides a comprehensive and up-to-date overview of the field, covering both theoretical foundations and practical applications. With its clear and concise writing style, it makes complex concepts accessible to a wide audience, making it an invaluable tool for anyone seeking to advance their knowledge in this area.

Weight: 559g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031076121
Edition number: 1st ed. 2022