Nuevo:
-12% US$60.70US$60.70
Entrega el martes, 15 de octubre
Enviado por: Amazon.com Vendido por: Amazon.com
Ahorra con Usado - Muy Bueno
US$56.99US$56.99
Este producto no se puede enviar al punto de entrega seleccionado. Selecciona un punto de entrega diferente.
Enviado por: Book Odyssey Vendido por: Book Odyssey
Descarga la app de Kindle gratis y comienza a leer libros Kindle al instante desde tu smartphone, tablet o computadora, sin necesidad de ningún dispositivo Kindle.
Lee al instante desde tu navegador con Kindle para la web.
Usando la cámara de tu celular escanea el siguiente código y descarga la aplicación Kindle.
Imagen no disponible
Color:
-
-
-
- Para ver la descarga de este video Flash Player
Seguir al autor
Aceptar
Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3) 1st Edición
Opciones de compra y productos Add-on
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.
- ISBN-101886529108
- ISBN-13978-1886529106
- Edición1er
- EditorialAthena Scientific
- Fecha de publicación1 Mayo 1996
- IdiomaInglés
- Dimensiones6.25 x 1 x 9.25 pulgadas
- Número de páginas512 páginas
Comprados juntos habitualmente

Títulos populares de este autor




Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive ControlTapa duraUS$9.66 de envíoSólo hay 8 disponible(s).
Opiniones editoriales
Críticas
"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
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
- Clasificación en los más vendidos de Amazon: nº1,160,591 en Libros (Ver el Top 100 en Libros)
- nº2,976 en Matemáticas (Libros)
- nº5,724 en Principal
- Opiniones de clientes:
Sobre el autor

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
- 5 estrellas4 estrellas3 estrellas2 estrellas1 estrella5 estrellas88%12%0%0%0%88%
- 5 estrellas4 estrellas3 estrellas2 estrellas1 estrella4 estrellas88%12%0%0%0%12%
- 5 estrellas4 estrellas3 estrellas2 estrellas1 estrella3 estrellas88%12%0%0%0%0%
- 5 estrellas4 estrellas3 estrellas2 estrellas1 estrella2 estrellas88%12%0%0%0%0%
- 5 estrellas4 estrellas3 estrellas2 estrellas1 estrella1 estrella88%12%0%0%0%0%
Las opiniones de clientes, incluidas las valoraciones de productos ayudan a que los clientes conozcan más acerca del producto y decidan si es el producto adecuado para ellos.
Para calcular la valoración global y el desglose porcentual por estrella, no utilizamos un promedio simple. En cambio, nuestro sistema considera cosas como la actualidad de la opinión y si el revisor compró el producto en Amazon. También analiza las opiniones para verificar la confiabilidad.
Más información sobre cómo funcionan las opiniones de clientes en AmazonOpiniones con imágenes
Great Content, but Received a Faded and Blurred Cover
-
Opiniones principales
Opiniones destacadas de los Estados Unidos
Ha surgido un problema al filtrar las opiniones justo en este momento. Vuelva a intentarlo en otro momento.
Calificado en Estados Unidos el 26 de septiembre de 2024
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
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)


