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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Applying reinforcement learning in finance

If we need to reiterate RL's promise, it is to obtain policies for sequential decision making to maximize rewards under uncertainty. What is a better match than finance for such a tool? In finance:

The goal is very much to maximize some monetary reward,
Decisions made now will definitely have consequences down the road,
Uncertainty is a defining factor.

As a result, RL is getting increasingly popular in the finance community.

To be clear, this section will not include any examples of a winning trading strategy, well, for obvious reasons: First, the author does not know any; second, even if he did, he would not tell that in a book (and no one would). In addition, there are challenges when it comes to using RL in finance. So, we start this section with a discussion on these challenges. Once we ground ourselves in reality, then we will proceed to defining some application areas and introducing some tools you can...