From Shortest Paths to Reinforcement Learning

1. Front Matter

   Pages i-xi

   PDF

2. The Dynamic Programming Principle

   • Paolo Brandimarte

   Pages 1-34

3. Implementing Dynamic Programming

   • Paolo Brandimarte

   Pages 35-66

4. Modeling for Dynamic Programming

   • Paolo Brandimarte

   Pages 67-97

5. Numerical Dynamic Programming for Discrete States

   • Paolo Brandimarte

   Pages 99-140

6. Approximate Dynamic Programming and Reinforcement Learning for Discrete States

   • Paolo Brandimarte

   Pages 141-160

7. Numerical Dynamic Programming for Continuous States

   • Paolo Brandimarte

   Pages 161-183

8. Approximate Dynamic Programming and Reinforcement Learning for Continuous States

   • Paolo Brandimarte

   Pages 185-204

9. Back Matter

   Pages 205-207

   PDF

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.

• 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

• DISMA, Politecnico di Torino, Torino, Italy

  Paolo Brandimarte

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.

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