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Stochastic Learning and Optimization : A Sensitivity-Based Approach / edited by Xi-Ren Cao
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: Boston, MA : Springer Science+Business Media, LLC, 2007Description: Online-Ressource (digital)ISBN:- 9780387690827
- 004.0151
- 519.23 22
- QA76.9.M35
- QA274.6
Contents:
Summary: 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.Summary: "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 (IAC) 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."PPN: PPN: 1646730763Package identifier: Produktsigel: ZDB-2-SCS
Front Matter; Introduction; Perturbation Analysis; Learning and Optimization with Perturbation Analysis; Markov Decision Processes; Sample-Path-Based Policy Iteration; Reinforcement Learning; Adaptive Control Problems as MDPs; Event-Based Optimization of Markov Systems; Constructing Sensitivity Formulas; Back Matter
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