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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Summary

In this chapter, we have covered an emerging paradigm in artificial intelligence, machine teaching, which is about effectively conveying the expertise of a subject matter expert (teacher) to machine learning model training. We discussed how this is similar to how humans are educated: By building on others' knowledge. The advantage of this approach is that it greatly increases data efficiency in machine learning, and, in some cases, makes learning possible that would have been impossible without a teacher. We discussed various methods in this paradigm, including reward function engineering, curriculum learning, demonstration learning, action masking, and concept networks. We observed how some of these methods improved vanilla use of Ape-X DQN significantly.

Besides its benefits, machine teaching also has some challenges and potential downsides: First, it is usually non-trivial to come up with good reward shaping, curriculum, set of action masking conditions etc. This...