Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

‘This book uniquely offers a comprehensive treatment of structural results for Partially Observable Markov Decision Processes (POMDPs), utilizing submodularity and stochastic orders. The new edition expands its scope by introducing essential results in nonparametric Bayes, stochastic optimization, and inverse reinforcement learning, making it an invaluable resource as both a textbook and reference.’

Bo Wahlberg - KTH Royal Institute of Technology, Sweden

‘This book is a tour-de-force on POMDPs and controlled sensing, featuring insightful treatment of foundational concepts in optimal filtering, stochastic control, and stochastic optimization. The new edition introduces innovative methods for detecting cognitive sensors through inverse reinforcement learning from a microeconomic perspective-critical for radar systems, signal processing, and control researchers.’

Muralidhar Rangaswamy - Air Force Research Laboratory, U.S.

‘An outstanding advanced graduate-level introduction to the increasingly important topic of partially observed Markov decision processes. The book is a delight to read-comprehensive, clear, up-to-date and insightful while preserving rigor. An essential resource for both researchers seeking to further advance the field and practitioners wishing to implement stochastic control in real engineering systems.’

Rob Evans - University of Melbourne