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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Curriculum learning

When we learn a new skill, we start with basics. Bouncing and dribbling are the first steps while learning basketball. Doing alley-oops is not something to try to teach in the first lesson. One needs to gradually proceed to advanced lessons, after feeling comfortable with the earlier ones. This idea of following a curriculum, from basics to advanced levels, is the basis of the whole education system. The question is whether machine learning models can benefit from the same approach. It turns out that they can!

In the context of RL, when we create a curriculum, we similarly start with "easy" environment configurations for the agent. This way the agent can get an idea about what success means early on, rather than spending a lot of time by blindly exploring the environment with the hope of stumbling upon success. We then gradually increase the difficulty if we observe the agent is exceeding a certain reward threshold. Each of these difficulty levels are...