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Author: Stephen J. Kahne
Publisher:
ISBN:
Category : Control theory
Languages : en
Pages : 30
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Book Description
The application of Bellman's dynamic programming technique to realistic control problems has generally been precluded by excessive storage requirements inherent in the method. In this paper, the notion of state mobility is described and shown to be valuable in reducing certain classes of dynamic programming calculations to manageable size. The scheme requires one simple calculation at each stage of the process. IN many cases even this calculation may be omitted. It results in the reduction of the range of allowable state variables to be scanned. The amount of reduction varies from problem to problem. A simple example exhibits a fifty percent reduction. This corresponds to a fifty percent reduction in storage requirements for the problem. Reductions of one or two orders of magnitude appear possible for certain classes of problems.
Author: Stephen J. Kahne
Publisher:
ISBN:
Category : Control theory
Languages : en
Pages : 30
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Book Description
The application of Bellman's dynamic programming technique to realistic control problems has generally been precluded by excessive storage requirements inherent in the method. In this paper, the notion of state mobility is described and shown to be valuable in reducing certain classes of dynamic programming calculations to manageable size. The scheme requires one simple calculation at each stage of the process. IN many cases even this calculation may be omitted. It results in the reduction of the range of allowable state variables to be scanned. The amount of reduction varies from problem to problem. A simple example exhibits a fifty percent reduction. This corresponds to a fifty percent reduction in storage requirements for the problem. Reductions of one or two orders of magnitude appear possible for certain classes of problems.
Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 0470182954
Category : Mathematics
Languages : en
Pages : 487
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Book Description
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Author: Donald A. Pierre
Publisher: Courier Corporation
ISBN: 0486136957
Category : Mathematics
Languages : en
Pages : 644
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Book Description
Broad-spectrum approach to important topic. Explores the classic theory of minima and maxima, classical calculus of variations, simplex technique and linear programming, optimality and dynamic programming, more. 1969 edition.
Author: Robert Edward Larson
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 354
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Book Description
Author:
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 908
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Book Description
Author: Lucian Busoniu
Publisher: CRC Press
ISBN: 1439821097
Category : Computers
Languages : en
Pages : 280
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Book Description
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
Author: D. P. Bertsekas
Publisher:
ISBN:
Category :
Languages : en
Pages : 387
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Book Description
Author: Richard E. Bellman
Publisher: Princeton University Press
ISBN: 1400874653
Category : Computers
Languages : en
Pages : 389
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Book Description
This comprehensive study of dynamic programming applied to numerical solution of optimization problems. It will interest aerodynamic, control, and industrial engineers, numerical analysts, and computer specialists, applied mathematicians, economists, and operations and systems analysts. Originally published in 1962. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Author: Air Force Cambridge Research Laboratories (U.S.)
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 322
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Book Description
Summaries are given of research in the following fields: upper atmosphere physics, microwave physics, space physics, terrestrial sciences, optical physics, data sciences, meteorology, solid state, aerospace instrumentation.
Author: Robert Edward Larson
Publisher: Elsevier Publishing Company
ISBN:
Category : Mathematics
Languages : en
Pages : 286
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Book Description
