This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to present a rig� orous treatment that combines two significant research topics: Stochastic Games and Markov Decision Processes, which have been studied exten� sively, and at times quite independently, by mathematicians, operations researchers, engineers, and economists. Since Markov decision processes can be viewed as a special noncompeti� tive case of stochastic games, we introduce the new terminology Competi� tive Markov Decision Processes that emphasizes the importance of the link between these two topics and of the properties of the underlying Markov processes. The book is designed to be used either in a classroom or for self-study by a mathematically mature reader. In the Introduction (Chapter 1) we outline a number of advanced undergraduate and graduate courses for which this book could usefully serve as a text. A characteristic feature of competitive Markov decision processes - and one that inspired our long-standing interest - is that they can serve as an "orchestra" containing the "instruments" of much of modern applied (and at times even pure) mathematics. They constitute a topic where the instruments of linear algebra, applied probability, mathematical program� ming, analysis, and even algebraic geometry can be "played" sometimes solo and sometimes in harmony to produce either beautifully simple or equally beautiful, but baroque, melodies, that is, theorems.

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Stochastic Games have been studied by mathematicians, operations researchers, electrical engineers, and economists since the 1950s; the simpler single-controller, noncompetitive version of these models evolved separately under the name of Markov Decision Processes. This book is devoted to a unified treatment of both subjects under the general heading of Competitive Markov Decision Processes. It examines these processes from the standpoints of modeling and of optimization, providing newcomers to the field with an accessible account of algorithms, theory, and applications, while also supplying specialists with a comprehensive survey of recent developments. Requiring only some knowledge of linear algebra and real analysis (further mathematical details are supplied in appendices), and limiting itself to finite-state discrete-time models, the book is suitable as a graduate text. Some of the more advanced topics may also be omitted without affecting the continuity of the presentation, making the text accessible to advanced undergraduates.

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