Markov Decision Processes in Practice

Markov Decision Processes in Practice
Author: Richard J. Boucherie
Publisher: Springer
Total Pages: 563
Release: 2017-03-10
Genre: Business & Economics
ISBN: 3319477668


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This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.


Markov Decision Processes in Practice
Language: en
Pages: 563
Authors: Richard J. Boucherie
Categories: Business & Economics
Type: BOOK - Published: 2017-03-10 - Publisher: Springer

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This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support appro
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Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning prob
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Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

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Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a lead
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Authors: Nicole Bäuerle
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The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spac
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Language: en
Pages: 213
Authors: Mausam
Categories: Computers
Type: BOOK - Published: 2012 - Publisher: Morgan & Claypool Publishers

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Provides a concise introduction to the use of Markov Decision Processes for solving probabilistic planning problems, with an emphasis on the algorithmic perspec