Overview
- Presents recent research in decision making under uncertainty, in particular reinforcement learning
- Relates the theory to practical problems in reinforcement learning, artificial intelligence, and cognitive science
- Gives a thorough understanding of statistical decision theory and the meaning of hypothesis testing
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 223)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in
introductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.
Authors and Affiliations
Bibliographic Information
Book Title: Decision Making Under Uncertainty and Reinforcement Learning
Book Subtitle: Theory and Algorithms
Authors: Christos Dimitrakakis, Ronald Ortner
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-031-07614-5
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-07612-1Published: 03 December 2022
Softcover ISBN: 978-3-031-10892-1Published: 07 December 2023
eBook ISBN: 978-3-031-07614-5Published: 02 December 2022
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XIII, 243
Number of Illustrations: 5 b/w illustrations, 62 illustrations in colour