You're reading from Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

Product type Paperback

Published in Dec 2020

Publisher Packt

ISBN-13 9781838644147

Length 544 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

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Table of Contents (24) Chapters

Preface

1. Section 1: Reinforcement Learning Foundations

2. Chapter 1: Introduction to Reinforcement Learning FREE CHAPTER

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

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Other applications of multi-armed and contextual bandits

So far, we have focused on online advertising example as our running example. If you are wondering how commonly bandit algorithms are used in practice for such problems, it is actually quite common. For example, Microsoft has a service, called Personalizer, based on bandit algorithms (disclaimer: the author is a Microsoft employee at the time of writing this book). The example here itself is inspired by the work at Hubspot – a marketing solutions company (Collier & Llorens, 2018). Moreover, bandit problems have a vast array of practical applications other than advertising. In this section we briefly go over some of those applications.

Recommender systems

The bandit problems we formulated and solved in this chapter are a type of recommender system: they recommend which ad to display, potentially leveraging the information available about the users. There are many other recommender systems that use bandits in a...