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Dynamic Programming and Optimal Control Hardcover – 29 January 2007

by Dimitri P. Bertsekas (Author)

21 ratings

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A two-volume set, consisting of the latest editions of the two volumes (4th edition (2017) for Vol. I, and 4th edition (2012) 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, as well as an up-to-date account of some of the most interesting developments in approximate dynamic programming. The second volume is oriented towards mathematical analysis and computation, treats infinite horizon problems extensively, and provides a detailed account of approximate large-scale dynamic programming and reinforcement learning. 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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Product details

Language : English
Hardcover : 1270 pages
ISBN-10 : 1886529086
ISBN-13 : 978-1886529083

Best Sellers Rank: 42,353 in Books (See Top 100 in Books)
- 30 in Mathematical Physics
- 429 in Computer Science

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Amazon.com: 8 reviews

Kim Do-Yup

many examples are very helpful for readers like me. However

28 August 2016 - Published on Amazon.com

Verified Purchase

I've read the textbook dealing with DP up to chapter 6. Since this book is approached mathematically, I think it is very well made except a few typos. A bit of unsatisfactoriness for me is the style of the book. I prefer the style introducing the general result first, and then proves why they are derived and where they are derived from. And then, many examples are very helpful for readers like me. However, this book introduces examples first, and then constructs the general form from and using the examples. I am sure that everybody has different styles. My word is just not my type. Nevertheless, the contents that textbook handles are wonderful.

5 people found this helpful

Dylan

So much knowledge!

15 March 2019 - Published on Amazon.com

Verified Purchase

This set of two books is just an absolute archive of knowledge. Everything you need to know on Optimal Control and Dynamic programming from beginner level to advanced intermediate is here. Plus worked examples are great. They aren't boring examples as well.
This set pairs well with Simulation-Based Optimization by Abhijit Gosavi.
I guess the point is, this book should be the central framework of any graduate course in optimal control and operations research.

One person found this helpful

sunflower

It is a good start to study reinforcement learning and its beyond

10 December 2017 - Published on Amazon.com

Verified Purchase

Prof. Bertsekas is definitely the top-level researcher in this domain. It is a good start to study reinforcement learning and its beyond.

One person found this helpful

Hayong Yun

Five Stars

15 March 2018 - Published on Amazon.com

Verified Purchase

A classic!

Abhijit Gosavi

Excellent book

28 August 2008 - Published on Amazon.com

*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.

19 people found this helpful

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