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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Challenges in meta-reinforcement learning

The main challenges regarding meta-RL, following (Rakelly, 2019), are as follows:

Meta-RL requires a meta-training step over various tasks, which are usually hand-crafted. A challenge here is to create an automated procedure to generate these tasks.
The exploration phase that is supposed to be learned during meta-training is in practice is not efficiently learned.
Meta-training involves sampling from an independent and identical distribution of tasks, which is not a realistic assumption. So, one goal is to make meta-RL more "online" by making it learn from a stream of tasks.

In addition to these challenges, it is important to note meta-RL methods will not work as well as the other methods, such as domain randomization, in complex tasks like robot hand manipulation. As the research in this area progresses, we can expect to see this gap to decrease and meta-RL make its way to mainstream with its unique advantages...