A two-volume set, consisting of the latest editions of the two volumes (2005 for Vol. I, 2007 for Vol. II). Much supplementary material can be found at the book's web page. 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, and treats infinite horizon problems extensively. This is a 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 introduction to the methodology of Neuro-Dynamic Programming, which is the focus of much recent research.
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Review of Vol. I, 3rd Edition: "In addition to being very well written and organized, the material has several special features that make the book unique in the class of introductory textbooks on dynamic programming. For instance, it presents both deterministic and stochastic control problems, in both discrete- and continuous-time, and it also presents the 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. ... In conclusion the book is highly recommendable for an introductory course on dynamic programming and its applications." --Onesimo Hernandez Lerma, in Math Reviews
"In this two-volume work Bertsekas caters equally effectively to theoreticians who care for proof of such concepts as the existence and the nature of optimal policies and to practitioners interested in the modeling and the quantitative and numerical solution aspects of stochastic dynamic programming." --Michael Caramanis, in Interfaces
"In conclusion, this book is an excellent source of reference ... The main strengths of the book are the clarity of the exposition, the quality and variety of the examples, and its coverage of the most recent advances." --T. W. Archibald, in IMA Jnl. of Mathematics
"In this two-volume work Bertsekas caters equally effectively to theoreticians who care for proof of such concepts as the existence and the nature of optimal policies and to practitioners interested in the modeling and the quantitative and numerical solution aspects of stochastic dynamic programming." --Michael Caramanis, in Interfaces
"In conclusion, this book is an excellent source of reference ... The main strengths of the book are the clarity of the exposition, the quality and variety of the examples, and its coverage of the most recent advances." --T. W. Archibald, in IMA Jnl. of Mathematics
"In this two-volume work Bertsekas caters equally effectively to theoreticians who care for proof of such concepts as the existence and the nature of optimal policies and to practitioners interested in the modeling and the quantitative and numerical solution aspects of stochastic dynamic programming." --Michael Caramanis, in Interfaces
"In conclusion, this book is an excellent source of reference ... The main strengths of the book are the clarity of the exposition, the quality and variety of the examples, and its coverage of the most recent advances." --T. W. Archibald, in IMA Jnl. of Mathematics
"In this two-volume work Bertsekas caters equally effectively to theoreticians who care for proof of such concepts as the existence and the nature of optimal policies and to practitioners interested in the modeling and the quantitative and numerical solution aspects of stochastic dynamic programming." --Michael Caramanis, in Interfaces
"In conclusion, this book is an excellent source of reference ... The main strengths of the book are the clarity of the exposition, the quality and variety of the examples, and its coverage of the most recent advances." --T. W. Archibald, in IMA Jnl. of Mathematics
About the Author
Dimitri Bertsekas is Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He has researched a broad variety of subjects from optimization theory, control theory, parallel and distributed computation, systems analysis, and data communication networks. He has written numerous papers in each of these areas, and he has authored or coauthored thirteen textbooks. He is a Fellow of the IEEE, and a member of the US National Academy of Engineering.
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9 of 10 people found the following review helpful:
5.0 out of 5 stars
Excellent book,
This review is from: Dynamic Programming and Optimal Control (2 Vol Set) (Hardcover)
*This is easily the best book on dynamic programming. It certainly is the most up-to-date book on this topic. The first volume covers numerous topics such as deterministic control, HJB equation for the deterministic case, Pontryagin principle, finite horizon MDPs, partially observable MDPs, and rollout heuristics. The second volume treats the infinite horizon case for the regular MDP --- average reward, discounted reward, semi-Markov control, and even some reinforcement learning.
*I love the notation. The proofs in this book are much easier than those you will find elsewhere. (This opinion is based on my study of proofs in other texts.) The treatment is very sophisticated and yet very accessible! Furthermore, what is really a bonus here --- something you won't find in the other books --- is a discussion on the stochastic shortest path (SSP). The SSP makes it so easy to analyze the average reward problem and the finite horizon problem with a stationary transition probability structure. *I strongly recommend this book to all readers interested in understanding the basics of DP and the convergence proofs underlying the DP machinery. It is a must on your book shelf if you are working on research in DP or topics related to DP such as reinforcement learning or adaptive (approximate) DP.
5 of 17 people found the following review helpful:
4.0 out of 5 stars
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This review is from: Dynamic Programming and Optimal Control (2 Vol Set) (Hardcover)
It's a good book for dynamic programming, though it requires substantial knowledge of mathematics.
1 of 13 people found the following review helpful:
5.0 out of 5 stars
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This review is from: Dynamic Programming and Optimal Control (2 Vol Set) (Hardcover)
really nice book on dynamic programming... easy to understand and contains all requisite details.
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