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Stochastic Learning and Optimization, 2007 A Sensitivity-Based Approach

Langue : Anglais

Auteur :

Couverture de l’ouvrage Stochastic Learning and Optimization

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 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. This new perspective on a popular topic is presented by a well respected expert in the field.

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