From Shortest Paths to Reinforcement Learning: A MATLAB-Based Tutorial on Dynamic Programming: EURO Advanced Tutorials on Operational Research
Autor Paolo Brandimarteen Limba Engleză Paperback – 12 ian 2022
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Specificații
ISBN-13: 9783030618698
ISBN-10: 3030618692
Pagini: 220
Ilustrații: XI, 207 p. 67 illus.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.31 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria EURO Advanced Tutorials on Operational Research
Locul publicării:Cham, Switzerland
ISBN-10: 3030618692
Pagini: 220
Ilustrații: XI, 207 p. 67 illus.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.31 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria EURO Advanced Tutorials on Operational Research
Locul publicării:Cham, Switzerland
Cuprins
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.
Notă biografică
Paolo Brandimarte is full professor at the Department of Mathematical Sciences of Politecnico di Torino, Italy, where he teaches courses on Business Analytics, Risk Management, and Operations Research. He is the author of more than ten books on the application of optimization and simulation methods to problems ranging from quantitative finance to production and supply chain management.
Textul de pe ultima copertă
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. 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.
Caracteristici
Covers both, classical numerical analysis approaches and more recent learning strategies based on Monte Carlo simulation Includes well-documented MATLAB code snapshots to illustrate algorithms and applications in detail Illustrate subtle modeling issues in detail Illustrates a wide set of applications Includes supplementary material: sn.pub/extras