Markov Decision Processes in Artificial Intelligence

✍ Scribed by Olivier Sigaud, Olivier Buffet

Publisher: Wiley-ISTE
Year: 2010
Tongue: English
Leaves: 466
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.Content:
Chapter 1 Markov Decision Processes (pages 1–38): Frederick Garcia and Emmanuel Rachelson
Chapter 2 Reinforcement Learning (pages 39–66): Olivier Sigaud and Frederick Garcia
Chapter 3 Approximate Dynamic Programming (pages 67–98): Remi Munos
Chapter 4 Factored Markov Decision Processes (pages 99–126): Thomas Degris and Olivier Sigaud
Chapter 5 Policy?Gradient Algorithms (pages 127–152): Olivier Buffet
Chapter 6 Online Resolution Techniques (pages 153–184): Laurent Peret and Frederick Garcia
Chapter 7 Partially Observable Markov Decision Processes (pages 185–228): Alain Dutech and Bruno Scherrer
Chapter 8 Stochastic Games (pages 229–276): Andriy Burkov, Laetitia Matignon and Brahim Chaib?Draa
Chapter 9 DEC?MDP/POMDP (pages 277–318): Aurelie Beynier, Francois Charpillet, Daniel Szer and Abdel?Illah Mouaddib
Chapter 10 Non?Standard Criteria (pages 319–360): Matthieu Boussard, Maroua Bouzid, Abdel?Illah Mouaddib, Regis Sabbadin and Paul Weng
Chapter 11 Online Learning for Micro?Object Manipulation (pages 361–374): Guillaume Laurent
Chapter 12 Conservation of Biodiversity (pages 375–394): Iadine Chades
Chapter 13 Autonomous Helicopter Searching for a Landing Area in an Uncertain Environment (pages 395–412): Patrick Fabiani and Florent Teichteil?Kunigsbuch
Chapter 14 Resource Consumption Control for an Autonomous Robot (pages 413–424): Simon Le Gloannec and Abdel?Illah Mouaddib
Chapter 15 Operations Planning (pages 425–452): Sylvie Thiebaux and Olivier Buffet

📜 SIMILAR VOLUMES

Markov Decision Processes in Practice

📁 Markov Decision Processes in Practice

✍ Richard J. Boucherie, Nico M. van Dijk 📂 Library 📅 2017 🏛 Springer 🌐 English

Examples in Markov Decision Processes

📁 Examples in Markov Decision Processes

✍ A B Piunovskiy 📂 Library 📅 2012 🏛 World Scientific Publishing Company 🌐 English

This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, un

Markov Decision Processes in Practice

📁 Markov Decision Processes in Practice

✍ Richard J. Boucherie, Nico M. van Dijk (eds.) 📂 Library 📅 2017 🏛 Springer International Publishing 🌐 English

<p>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. Th

Markov Decision Processes

📁 Markov Decision Processes

✍ D. J. White 📂 Library 📅 1993 🏛 John Wiley & Sons 🌐 English

Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination of solutions or their properties. Coverage includes optimal equations, algorithms and their characteristics, probability distributions, modern development in the Mar

Markov decision processes

📁 Markov decision processes

✍ D. J. White 📂 Library 📅 1993 🏛 John Wiley & Sons 🌐 English

Markov decision processes

📁 Markov decision processes

✍ D. J. White 📂 Library 📅 1993 🏛 John Wiley & Sons 🌐 English

An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified