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

23. Other Books You May Enjoy

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Open source implementations of policy-gradient methods

In this chapter we have covered many algorithms. It is not quite possible to explicitly implement all these algorithms given the space limitations here. We instead relied on RLlib implementations to train agents for our use case. RLlib is open source, so you can go to https://github.com/ray-project/ray/tree/releases/1.0.1/rllib and dive into implementations of these algorithms.

Having said that, RLlib implementations is built for production systems and therefore involve many other implementations regarding error-handling, and preprocessing. In addition, there is a lot of code reuse, resulting in implementations of with multiple class inheritances. A much easier set of implementations is provided by OpenAI's Spinning Up repo at https://github.com/openai/spinningup. I highly recommend you go into that repo and dive into the implementation details of these algorithms we discussed in this chapter.

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OpenAI Spinning...