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). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
About the author
Olivier Sigaud is a Professor of Computer Science at the University of Paris 6 (UPMC). He is the Head of the ‘Motion’ Group in the Institute of Intelligent Systems and Robotics (ISIR).
Olivier Buffet has been an INRIA researcher in the Autonomous Intelligent Machines (MAIA) team of the LORIA laboratory, since November 2007.
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