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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What you have achieved with this book

First of all, congratulations! You have come a long way to go beyond the fundamentals and to acquire the skills and the mindset to apply reinforcement learning in real-world. Here is what we have done together in this book:

We have spent a fair amount of time on bandit problems, which have tremendous number of applications in industry and academia.
We have gone deeper into the theory than a typical applied book to strengthen your foundation in RL.
We have covered many of the algorithms and architectures behind the most successful applications of RL.
We have discussed advanced training strategies to get the most out of the advanced RL algorithms.
We have done hands-on work with realistic case studies.
Throughout this journey, we have both implemented our versions of some of the algorithms, as well as utilized libraries, such as Ray and RLlib, which power many teams and platforms at the top tech companies for their reinforcement...