Counterexamples in Markov Decision Processes

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Counterexamples in Markov Decision Processes is written by A. B. Piunovskiy and published by Wspc (Europe). It's available with International Standard Book Number or ISBN identification 1800616759 (ISBN 10) and 9781800616752 (ISBN 13).

""This remarkable and intriguing book is highly recommended. Some examples are aimed at undergraduate students, whilst others will be of interest to advanced undergraduates, graduates and research students in probability theory, optimal control and applied mathematics, looking for a better understanding of the theory; experts in Markov decision processes, professional or amateur researchers. Active researchers can refer to this book on applicability of mathematical methods and theorems." The European Mathematical Society "The book presents many interesting topics and results. This is an important book that will be particularly useful to students and researchers on MDPs. I recommend it to anyone interested in the theory of MDPs." Mathematical Reviews Markov Decision Processes (MDPs) form a cornerstone of applied probability, with over 50 years of rich research history. Throughout this time, numerous foundational books and thousands of journal articles have shaped the field. The central objective of MDP theory is to identify the optimal control strategy for Markov random processes with discrete time. Interestingly, the best control strategies often display unexpected or counterintuitive behaviors, as documented by a wide array of studies. This book gathers some of the most compelling examples of such phenomena while introducing new ones. By doing so, it serves as a valuable companion to existing textbooks. While many examples require little to no prior knowledge, others delve into advanced topics and will primarily interest specialists. In this second edition, extensive revisions have been made, correcting errors and refining the content, with a wealth of new examples added. The range of examples spans from elementary to advanced, requiring background knowledge in areas like measure theory, convex analysis, and advanced probability. A new chapter on continuous time jump processes has also been introduced. The entire text has been reworked for clarity and accessibility. This book is an essential resource for active researchers and graduate students in the field of Markov Decision Processes"-- Provided by publisher.