Dynamic Programming and Optimal Control. 3rd Edition, Volume II by. Dimitri P. Bertsekas. Massachusetts Institute of Technology. Chapter 6. Dimitri P. Bertsekas undergraduate studies were in engineering at the Optimization Theory” (), “Dynamic Programming and Optimal Control,” Vol. View colleagues of Dimitri P. Bertsekas Benjamin Van Roy, John N. Tsitsiklis, Stable linear approximations to dynamic programming for stochastic control.
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I, 4th EditionVol. II, 4th edition Vol.
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 programmingwhich 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 dynamkc 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 optimmal 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 progeamming 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 p.ebrtsekas 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. PhD students and post-doctoral researchers will find Prof. Bertsekas’ book to be a very useful reference to which they will orogramming back time and again to find an obscure reference to related work, use one of the examples in their own papers, and draw inspiration from the deep connections exposed between major techniques.
Dynamic Programming and Optimal Control
prrogramming Undergraduate students should definitely first try the online lectures and decide if they are ready for the ride.
Between this and the first volume, there is an amazing diversity of ideas presented in a unified and accessible manner. This new edition offers an expanded treatment of approximate dynamic programming, synthesizing a substantial and growing research literature on the topic.
Among its special features, the book: I and II, 3rd Edition: The coverage is significantly expanded, refined, and brought up-to-date. The book is a rigorous yet highly readable and comprehensive source on all aspects kptimal to DP: It should be viewed as the principal DP textbook and reference work at present. With its rich mixture of theory and applications, its many examples and opitmal, its unified treatment of the subject, and its polished presentation style, it is eminently suited for classroom use or self-study.
For instance, it presents both deterministic and stochastic control problems, iptimal both discrete- and continuous-time, and it also presents prograkming Pontryagin minimum principle for deterministic systems together with several extensions. It contains problems with perfect and imperfect information, as well as minimax control methods also known as worst-case control problems or games against nature.
I also has a full chapter on suboptimal control and many related techniques, such as open-loop feedback controls, limited lookahead policies, rollout algorithms, and model predictive control, to name a few.
Textbook: Dynamic Programming and Optimal Control
In conclusion the book is highly recommendable for an introductory course on dynamic programming and its applications. The main strengths of the book are the clarity of the exposition, the quality and variety of optiml examples, and its coverage of the most recent advances. Archibald, in IMA Jnl. It is a valuable reference for control theorists, mathematicians, and all those who use systems and control theory in p.bertsemas work. Students will for sure find the approach very readable, clear, and concise.
Misprints are extremely few. He is the recipient of the A. He has been teaching the material included in this book in introductory graduate courses for more than forty years.