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Record Nr. |
UNINA9910484119403321 |
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Autore |
Brandimarte Paolo |
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Titolo |
From Shortest Paths to Reinforcement Learning : A MATLAB-Based Tutorial on Dynamic Programming / / by Paolo Brandimarte |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XI, 207 p. 67 illus.) |
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Collana |
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EURO Advanced Tutorials on Operational Research, , 2364-6888 |
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Disciplina |
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Soggetti |
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Operations research |
Management science |
Econometrics |
Numerical analysis |
Social sciences - Mathematics |
Industrial Management |
Operations Research and Decision Theory |
Operations Research, Management Science |
Quantitative Economics |
Numerical Analysis |
Mathematics in Business, Economics and Finance |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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The dynamic programming principle -- Implementing dynamic programming -- Modeling for dynamic programming -- Numerical dynamic programming for discrete states -- Approximate dynamic programming and reinforcement learning for discrete states -- Numerical dynamic programming for continuous states -- Approximate dynamic programming and reinforcement learning for continuous states. |
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Sommario/riassunto |
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Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. |
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Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics. |
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