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Identifies a unified framework for performance optimization
Presents a new perspective on a popular topic by a well respected expert in the field
Combines currently prominent research on reinforcement learning / neuro-dynamic programming with a unique research approach based on sensitivity analysis and discrete-event systems concepts
Stochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science. This book is unique in the following aspects.
(Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework.
(A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas. This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting.
(Event-based optimization) This book introduces the recently developed event-based optimization approach, which opens up a research direction in overcoming or alleviating the difficulties due to the curse of dimensionality issue by utilizing the system's special features.
(Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction.
Content Level »Research
Keywords »Computer - Markov Chains - Markov decision processes - Operations Research - calculus - ergodic systems - event based optimization - identification and adaptive control - optimization - perturbation analysis - programming - queueing systems - reinforcement learning - robot - stochastic approximation
Four Disciplines in Learning and Optimization.- Perturbation Analysis.- Learning and Optimization with Perturbation Analysis.- Markov Decision Processes.- Sample-Path-Based Policy Iteration.- Reinforcement Learning.- Adaptive Control Problems as MDPs.- The Event-Based Optimization - A New Approach.- Event-Based Optimization of Markov Systems.- Constructing Sensitivity Formulas.