This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applicatio

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning prob

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding

In a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control problems, with uncountab

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engi