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

by Paolo Brandimarte

Overview

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.

Product Details

ISBN-13:	9783030618667
Publisher:	Springer International Publishing
Publication date:	01/12/2021
Series:	EURO Advanced Tutorials on Operational Research
Edition description:	1st ed. 2021
Pages:	207
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

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.

Table of Contents

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.