Stochastic Learning and Optimization
A Sensitivity-Based Approach
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Beschreibung
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.
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 combines currently prominent research on reinforcement learning / neuro-dynamic programming with a unique research approach based on sensitivity analysis and discrete-event systems concepts. This new perspective on a popular topic is presented 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 Presents a new perspective on a popular topic by a well respected expert in the field
Autor*in
Xi-Ren Cao
Themen in »Stochastic Learning and Optimization«
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
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From the reviews:
"The book is written by known contributor to the theory of Markov decision problems and the theory of queueing systems and it is chiefly based on recent results obtained by the author. … The book provide good introductory materials for graduate students and engineers who wish to have an overview of learning and optimization theory, the related methodologies in different disciplines and their relations. Moreover, the book is useful in finding new research topics and in practical applications." (Vladimir Sobolev, Zentralblatt MATH, Vol. 1130, 2008)
"The systems studied in this book are stochastic dynamic systems … . The book is very well written, and … they are often presented in an intuitive way so that the study is really enjoyable. … the subject of the book is very important and very interesting. … It is intended for teachers, researchers, and graduate students who can recognize the practical and theoretical value of the methods described … . strongly recommended for scholars in engineering, mathematics, computer science, artificial intelligence, and machine learning." (Lefteris Angelis, ACM Computing Reviews, Vol. 49 (12), December, 2008)
"The key point of this monograph is perturbation analysis … . The book has appendices on Markov processes, stochastic matrices and queueing theory. Every chapter contains a number of problems for self-study. Along with known/proved statements, the reader can find many open problems for future research. Finally, the book can become the basis for several undergraduate lecture courses." (Aleksey B. Piunovskiy, Mathematical Reviews, Issue 2009 f)
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