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Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3) 1st Edición

4.9 4.9 de 5 estrellas 18 calificaciones

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This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.

Neuro-dynamic programming uses neural network approximations to overcome the "curse of dimensionality" and the "curse of modeling" that have been the bottlenecks to the practical application of dynamic programming and stochastic control to complex problems. The methodology allows systems to learn about their behavior through simulation, and to improve their performance through iterative reinforcement.

This book provides the first systematic presentation of the science and the art behind this exciting and far-reaching methodology.

The book develops a comprehensive analysis of neuro-dynamic programming algorithms, and guides the reader to their successful application through case studies from complex problem areas.

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Neurodynamic Programming is a remarkable monograph that integrates a sweeping mathematical and computational landscape into a coherent body of rigorous knowledge. The topics are current, the writing is clear and to the point, the examples are comprehensive and the historical notes and comments are scholarly."

"In this monograph, Bertsekas and Tsitsiklis have performed a Herculean task that will be studied and appreciated by generations to come. I strongly recommend it to scientists and engineers eager to seriously understand the mathematics and computations behind modern behavioral machine learning. --George Cybenko in IEEE Computational Science and Engineering, May 1998:

Biografía del autor

The authors are Professors of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and members of the National Academy of Engineering.

Detalles del producto

  • Editorial ‏ : ‎ Athena Scientific; 1er edición (1 Mayo 1996)
  • Idioma ‏ : ‎ Inglés
  • Tapa dura ‏ : ‎ 512 páginas
  • ISBN-10 ‏ : ‎ 1886529108
  • ISBN-13 ‏ : ‎ 978-1886529106
  • Dimensiones ‏ : ‎ 6.25 x 1 x 9.25 pulgadas
  • Opiniones de clientes:
    4.9 4.9 de 5 estrellas 18 calificaciones

Sobre el autor

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Dimitri P. Bertsekas
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DIMITRI P. BERTSEKAS

Biographical Sketch

Dimitri P. Bertsekas undergraduate studies were in engineering at the National Technical University of Athens, Greece. He obtained his MS in electrical engineering at the George Washington University, Wash. DC in 1969, and his Ph.D. in system science in 1971 at the Massachusetts Institute of Technology.

Dr. Bertsekas has held faculty positions with the Engineering-Economic Systems Dept., Stanford University (1971-1974) and the Electrical Engineering Dept. of the University of Illinois, Urbana (1974-1979). Since 1979 he has been teaching at the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (M.I.T.), where he is currently McAfee Professor of Engineering. He has held editorial positions in several journals. His research at M.I.T. spans several fields, including optimization, control, large-scale computation, and data communication networks, and is closely tied to his teaching and book authoring activities. He has written numerous research papers, and sixteen books and research monographs, several of which are used as textbooks in MIT classes.

Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book "Neuro-Dynamic Programming" (co-authored with John Tsitsiklis), the 2000 Greek National Award for Operations Research, the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award for "contributions to the foundations of deterministic and stochastic optimization-based methods in systems and control," the 2014 Khachiyan Prize for Life-Time Accomplishments in Optimization, and the SIAM/MOS 2015 George B. Dantzig Prize. In 2001, he was elected to the United States National Academy of Engineering for "pioneering contributions to fundamental research, practice and education of optimization/control theory, and especially its application to data communication networks."

Dr. Bertsekas' recent books are "Introduction to Probability: 2nd Edition" (2008), "Convex Optimization Theory" (2009), "Dynamic Programming and Optimal Control, Vol. I, (2017), and Vol. II: Approximate Dynamic Programming" (2012), "Abstract Dynamic Programming" (2013), and "Convex Optimization Algorithms" (2015), all published by Athena Scientific.

Besides his professional activities, Professor Bertsekas is interested in travel, portrait, and landscape photography. His pictures have been exhibited on several occasions at M.I.T., and can also be accessed from his www site.

Opiniones de clientes

4.9 de 5 estrellas
18 calificaciones globales
Great Content, but Received a Faded and Blurred Cover
4 de 5 estrellas
Great Content, but Received a Faded and Blurred Cover
Neuro-Dynamic Programming is an excellent book with outstanding content, and the overall quality is very good. However, the copy I received has some issues with the cover. The colors appear faded, as if the book had been left exposed to the sun, and the letters on the cover are slightly blurred. This might be understandable for a copy from 1996, but this is part of the new 2021 reprint, which makes these flaws surprising. Despite these issues, I highly recommend the book for its valuable content.
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Opiniones destacadas de los Estados Unidos

Calificado en Estados Unidos el 20 de enero de 2020
Great book. Bear in mind this is an advanced book in reinforcement learning.
Calificado en Estados Unidos el 30 de abril de 2017
The updated version is a definitive treatise of the math that go along with the ideas of reinforcement learning, approximate dynamic programming. Thoroughly enjoying the book.
Calificado en Estados Unidos el 17 de agosto de 2018
Excellent text book for those who are interested in mathematical foundations of reinforcement learning.
Calificado en Estados Unidos el 31 de octubre de 2017
Great book!
Calificado en Estados Unidos el 26 de septiembre de 2024
Neuro-Dynamic Programming is an excellent book with outstanding content, and the overall quality is very good. However, the copy I received has some issues with the cover. The colors appear faded, as if the book had been left exposed to the sun, and the letters on the cover are slightly blurred. This might be understandable for a copy from 1996, but this is part of the new 2021 reprint, which makes these flaws surprising. Despite these issues, I highly recommend the book for its valuable content.
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4.0 de 5 estrellas Great Content, but Received a Faded and Blurred Cover
Calificado en Estados Unidos el 26 de septiembre de 2024
Neuro-Dynamic Programming is an excellent book with outstanding content, and the overall quality is very good. However, the copy I received has some issues with the cover. The colors appear faded, as if the book had been left exposed to the sun, and the letters on the cover are slightly blurred. This might be understandable for a copy from 1996, but this is part of the new 2021 reprint, which makes these flaws surprising. Despite these issues, I highly recommend the book for its valuable content.
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Calificado en Estados Unidos el 8 de octubre de 2015
New one. I like it.
Calificado en Estados Unidos el 15 de diciembre de 2007
Neuro-Dynamic Programming was, and is, a foundational reference for anyone wishing to work in the field that goes under names such as approximate dynamic programming, adaptive dynamic programming, reinforcement learning or, as a result of this book, neuro-dynamic programming. This is a clearly written treatment of the theory behind methods to solve dynamic programs by approximating the value function (in this book, the cost-to-go function).

The book is primarily for doctoral students and researchers. It provides descriptions of many solution strategies, but not at the level of detailed recipes. The presentation focuses on theory, but at a very readable level. Building on the prior work of the authors, this is the first book that brings together approximation methods in dynamic programming, with the theory of stochastic approximation methods (with its origins in Robbins and Monro) that provide the foundation for convergence proofs.

This book, along with Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) by Sutton and Barto, were major references when I started my own work in this field, leading up to my book: Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics) published by John Wiley and Sons. My students still use Neuro-Dynamic Programming as a reference for their research.

Warren Powell
Professor
Operations Research and Financial Engineering
Princeton University
A 44 personas les resultó útil
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Calificado en Estados Unidos el 12 de agosto de 2009
This was one of my favorite books as a student and it still is. The book presents much of the theory underlying reinforcement learning (the authors like to call it neuro-dynamic programming) in a clear and compact manner. While there are detailed mathematical proofs in many chapters, the book is actually pretty easy to read! It for sure helped me build an intuitive understanding of the mechanism of reinforcement learning when I was a student. The book is certaintly useful for beginners to this area; Ph.D. students are likely to get even more out of it.

Some special features of the book are:

* A clear discussion on the connection between classical dynamic programming and reinforcement learning (RL) along with the links to the Robbins-Monro algorithm
* Discussion on numerous types of approximate policy iteration
* A clear discussion on TD(lambda)
* Extensions to average reward (cost) problems
* A rigorous discussion of how function approximation works within this framework
* Treatment of the stochastic shortest path problem
* Detailed proofs of convergence of numerous NDP/RL algorithms
* Material/ideas on numerous topics on NDP/RL (for future research)
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