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Dynamic programming and optimal control

By: Material type: TextTextPublication details: Massachusetts Athena Scientific 2020Edition: 4th edDescription: xx, 555pISBN:
  • 9781886529434 (hb.)
Subject(s): DDC classification:
  • 519.703 BER
Summary: 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.
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Item type Current library Collection Call number Vol info Status Date due Barcode
Book Book Plaksha University Library Mathematics 519.703 BER (Browse shelf(Opens below)) Volume I Available 002990
Book Book Plaksha University Library Mathematics 519.703 BER (Browse shelf(Opens below)) Volume I Available 002991

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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.

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