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 and future directions

You could be wondering why we are back to talking about RL challenges after finishing an advanced-level book on this topic. Indeed, throughout the book, we presented many approaches to mitigate them. On the other hand, we cannot claim these challenges are solved. So, it is important to call them out and discuss the future directions for each in a concise list to give you a mental map and a compass to navigate through them.

Let's start our discussion with one of the most important challenges: Sample efficiency.

Sample efficiency

As you are now well aware, it takes a lot of data to train an RL model. OpenAI Five, who became a world-class player in the strategy game Dota 2, took 128,000 CPUs and 256 CPUs to train, over many months, collecting a total of 900 years' worth of game experience per day (OpenAI, 2018). RL algorithms are benchmarked on their performances after trained over 10 billion Atari frames (Kapturowski, 2019). This is...