Dynamic Programming and Optimal Control. 4th Edition, Volume II by. Dimitri P. Bertsekas. Massachusetts Institute of Technology. Chapter 4. Dynamic programming and optimal control: vols 1 & 2. Dimitri P. Bertsekas. Athena Scientific, Belmont, Mass. (). Vol. 1: pp., ISBN , $ Dynamic Programming and Optimal Control. by Dimitri P. Bertsekas. ISBNs: 1- (Vol. I, 4th Edition), (Vol. II, 4th Edition).
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Bertsekas, Dimitri P. Dynamic Programming and Optimal Control. Includes Bibliography and Index. 1. Mathematical Optimization. 2. Dynamic Programming. PDF | On Jan 1, , D P Bertsekas and others published Dynamic Programming and Optimal Control. Dynamic Programming and Optimal Control. 3rd Edition .. with feature extraction mappings (see Bertsekas and Tsitsiklis [BeT96], or. Sutton and ( See teshimaryokan.info
University of Edinburgh. Bertsekas book is an essential contribution that provides practitioners with a 30, feet view in Volume I - the second volume takes a closer look at the specific algorithms, strategies and heuristics used - of the vast literature generated by the diverse communities that pursue the advancement of understanding and solving control problems. Review of Vols. Issue Section:. Review of Vol.
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You do not currently have access to this article. Download all figures. II, 4th edition Vol. I ISBN II, i. II, 4th ed. 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.
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. This extensive work, aside from its focus on the mainstream dynamic programming and optimal control topics, relates to our Abstract Dynamic Programming Athena Scientific, , a synthesis of classical research on the foundations of dynamic programming with modern approximate dynamic programming theory, and the new class of semicontractive models, Stochastic Optimal Control: The Discrete-Time Case Athena Scientific, , which deals with the mathematical foundations of the subject, Neuro-Dynamic Programming Athena Scientific, , which develops the fundamental theory for approximation methods in dynamic programming, and Introduction to Probability 2nd Edition, Athena Scientific, , which provides the prerequisite probabilistic background.
New features of the 4th edition of Vol. I see the Preface for details: II see the Preface for details: Contains a substantial amount of new material, as well as a reorganization of old material. Volume II now numbers more than pages and is larger in size than Vol.
It can arguably be viewed as a new book! A major expansion of the discussion of approximate DP neuro-dynamic programming , which allows the practical application of dynamic programming to large and complex problems. Approximate DP has become the central focal point of this volume. Extensive new material, the outgrowth of research conducted in the six years since the previous edition, has been included. The first account of the emerging methodology of Monte Carlo linear algebra, which extends the approximate DP methodology to broadly applicable problems involving large-scale regression and systems of linear equations.
Expansion of the theory and use of contraction mappings in infinite state space problems and in neuro-dynamic programming. Bertsekas book is an essential contribution that provides practitioners with a 30, feet view in Volume I - the second volume takes a closer look at the specific algorithms, strategies and heuristics used - of the vast literature generated by the diverse communities that pursue the advancement of understanding and solving control problems.
This is achieved through the presentation of formal models for special cases of the optimal control problem, along with an outstanding synthesis or survey, perhaps that offers a comprehensive and detailed account of major ideas that make up the state of the art in approximate methods. The book ends with a discussion of continuous time models, and is indeed the most challenging for the reader. Still I think most readers will find there too at the very least one or two things to take back home with them.
Each Chapter is peppered with several example problems, which illustrate the computational challenges and also correspond either to benchmarks extensively used in the literature or pose major unanswered research questions. At the end of each Chapter a brief, but substantial, literature review is presented for each of the topics covered.
This is a book that both packs quite a punch and offers plenty of bang for your buck. Graduate students wanting to be challenged and to deepen their understanding will find this book useful.