From Shortest Paths to Reinforcement Learning
A MATLAB-Based Tutorial on Dynamic Programming
Textbook © 2021
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Brandimarte, Paolo 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.
Keywords: Dynamic programming, Reinforcement learning, Machine learning, Stochastic optimization, Dynamic optimization, Numerical optimization methods, Approximate dynamic programming, Monte Carlo simulation, Decision rules, Optimal control, Revenue management, Option pricing, Asset allocation, Inventory management, Resource budgeting, MATLAB programming, Parallel computing, quantitative finance, Engineering Economics
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 International License and the GNU Free Documentation License (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
