Dynamic Programming And Optimal Control

Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9781886529304
Size: 28.49 MB
Format: PDF
View: 1370
Download
"The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning. The first volume is oriented towards modeling, conceptualization, and finite-horizon problems, but also includes a substantive introduction to infinite horizon problems that is suitable for classroom use. The second volume is oriented towards mathematical analysis and computation, treats infinite horizon problems extensively, and provides an up-to-date account of approximate large-scale dynamic programming and reinforcement learning. The text contains many illustrations, worked-out examples, and exercises."--Publisher's website.

Neuro Dynamic Programming

Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9781886529106
Size: 23.86 MB
Format: PDF, Docs
View: 6148
Download
Neuro-dynamic programming, also known as reinforcement learning, is a recent methodology that can be used to solve very large and complex stochastic decision and control problems. It combines simulation, learning, neural networks or other approximation architectures, and the central ideas in dynamic programming. This book provides the first systematic presentation of the science and the art behind this promising methodology. It presents and unifies a large number of NDP methods, including several that are new; provides a rigorous development of the mathematical principles behind NDP; illustrates through case studies the practical application of NDP to complex problems and includes extensive background on dynamic programming and neural network training.

Dynamic Programming

Author: Richard Bellman
Publisher: Courier Corporation
ISBN: 0486317196
Size: 56.72 MB
Format: PDF, Docs
View: 3170
Download
Introduction to mathematical theory of multistage decision processes takes a "functional equation" approach. Topics include existence and uniqueness theorems, optimal inventory equation, bottleneck problems, multistage games, Markovian decision processes, and more. 1957 edition.

Dynamic Programming And Its Application To Optimal Control

Author:
Publisher: Elsevier
ISBN: 9780080955896
Size: 35.62 MB
Format: PDF, ePub, Docs
View: 4866
Download
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering

Reinforcement Learning And Approximate Dynamic Programming For Feedback Control

Author: Frank L. Lewis
Publisher: John Wiley & Sons
ISBN: 1118453972
Size: 42.92 MB
Format: PDF, ePub, Mobi
View: 4946
Download
Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

Linear Network Optimization

Author: Dimitri P. Bertsekas
Publisher: MIT Press
ISBN: 9780262023344
Size: 25.49 MB
Format: PDF, Docs
View: 5685
Download
Large-scale optimization is becoming increasingly important for students and professionals in electrical and industrial engineering, computer science, management science and operations research, and applied mathematics. Linear Network Optimization presents a thorough treatment of classical approaches to network problems such as shortest path, max-flow, assignment, transportation, and minimum cost flow problems. It is the first text to clearly explain important recent algorithms such as auction and relaxation, proposed by the author and others for the solution of these problems. Its coverage of both theory and implementations make it particularly useful as a text for a graduate-level course on network optimization as well as a practical guide to state-of-the-art codes in the field. Bertsekas focuses on the algorithms that have proved successful in practice and provides FORTRAN codes that implement them. The presentation is clear, mathematically rigorous, and economical. Many illustrations, examples, and exercises are included in the text. Dimitri P. Bertsekas is Professor of Electrical Engineering and Computer Science at MIT. Contents: Introduction. Simplex Methods. Dual Ascent Methods. Auction Algorithms. Performance and Comparisons. Appendixes.