Stochastic Learning and Optimization, Softcover reprint of hardcover 1st ed. 2007
A Sensitivity-Based Approach

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Language: English

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566 p. · 15.5x23.5 cm · Paperback

Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied.

This is a multi-disciplinary area which has been attracting wide attention across many disciplines. Areas such as perturbation analysis (PA) in discrete event dynamic systems (DEDSs), Markov decision processes (MDPs) in operations research, reinforcement learning (RL) or neuro-dynamic programming (NDP) in computer science, identification and adaptive control (I&AC) in control systems, share the common goal: to make the "best decision" to optimize system performance.

This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework.

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.
Combines currently prominent research on reinforcement learning / neuro-dynamic programming with a unique research approach based on sensitivity analysis and discrete-event systems concepts Presents a new perspective on a popular topic by a well respected expert in the field