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.

From Shortest Paths to Reinforcement Learning: A MATLAB-Based Introduction to Dynamic Programming / Brandimarte, Paolo. - STAMPA. - (2021), pp. 1-207.

From Shortest Paths to Reinforcement Learning: A MATLAB-Based Introduction to Dynamic Programming

Brandimarte Paolo
2021

Abstract

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.
978-3-030-61867-4
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2867018