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Mastering Reinforcement Learning with Python

You're reading from  Mastering Reinforcement Learning with Python

Product type Book
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Pages 544 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Enes Bilgin Enes Bilgin
Profile icon Enes Bilgin
LinkedIn
Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
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Table of Contents (24) Chapters

Preface 1. Section 1: Reinforcement Learning Foundations
2. Chapter 1: Introduction to Reinforcement Learning 3. Chapter 2: Multi-Armed Bandits 4. Chapter 3: Contextual Bandits 5. Chapter 4: Makings of a Markov Decision Process 6. Chapter 5: Solving the Reinforcement Learning Problem 7. Section 2: Deep Reinforcement Learning
8. Chapter 6: Deep Q-Learning at Scale 9. Chapter 7: Policy-Based Methods 10. Chapter 8: Model-Based Methods 11. Chapter 9: Multi-Agent Reinforcement Learning 12. Section 3: Advanced Topics in RL
13. Chapter 10: Introducing Machine Teaching 14. Chapter 11: Achieving Generalization and Overcoming Partial Observability 15. Chapter 12: Meta-Reinforcement Learning 16. Chapter 13: Exploring Advanced Topics 17. Section 4: Applications of RL
18. Chapter 14: Solving Robot Learning 19. Chapter 15: Supply Chain Management 20. Chapter 16: Personalization, Marketing, and Finance 21. Chapter 17: Smart City and Cybersecurity 22. Chapter 18: Challenges and Future Directions in Reinforcement Learning 23. Other Books You May Enjoy

Training your agent with Monte Carlo methods

Let's say you would like to learn the chance of flipping heads with a particular, possibly biased, coin:

  • One way of calculating this is through a careful analysis of the physical properties of the coin. Although this could give you the precise probability distribution of the outcomes, it is far from being a practical approach.
  • Alternatively, you can just flip the coin many times and look at the distribution in your sample. Your estimate could be a bit off if you don't have a large sample, but it will do the job for most practical purposes. The math you need to deal with using the latter method will be incomparably simpler.

Just like in the coin example, we can estimate the state values and action values in an MDP from random samples. Monte Carlo (MC) estimation is a general concept that refers to making estimations through repeated random sampling. In the context of RL, it refers to a collection of methods...

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Authors (1)

Profile icon Enes Bilgin
LinkedIn
Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
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