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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

Reinforcement Learning

Author (1):

Enes Bilgin

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

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Summary

This was our refresher on RL fundamentals! We began this chapter by discussing what RL is, and why it is such a hot topic and the next frontier in AI. We talked about some of the many possible applications of RL and the success stories that made it to the news headlines over the past several years. We defined the fundamental concepts we will use throughout the book. Finally, we covered the hardware and software you need to run the algorithms we will introduce in the next sections. Everything so far was to refresh your mind about RL, motivate and set you up for what is upcoming next: Implementing advanced RL algorithms to solve challenging real-world problems. In the next chapter, we will dive right into it with multi-armed bandit problems, an important class of RL algorithms that has many applications in personalization and advertising.

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Mastering Reinforcement Learning with Python

Published in: Dec 2020 Publisher: Packt ISBN-13: 9781838644147

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Authors (1)

Enes Bilgin

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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