Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Reinforcement Learning with Python

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

Arrow left icon
Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Length 544 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Enes Bilgin Enes Bilgin
Author Profile Icon Enes Bilgin
Enes Bilgin
Arrow right icon
View More author details
Toc

Table of Contents (24) Chapters Close

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 23. Other Books You May Enjoy

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.

You have been reading a chapter from
Mastering Reinforcement Learning with Python
Published in: Dec 2020
Publisher: Packt
ISBN-13: 9781838644147
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image