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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Implementing scalable deep Q-learning algorithms using Ray

In this section, we will implement a parallelized DQN variate using the Ray library. Ray is a powerful, general-purpose, yet simple framework for building and running distributed applications on a single machine as well as on large clusters. Ray has been built for applications that have heterogenous computational needs in mind. This is exactly what modern DRL algorithms require as they involve a mix of long and short running tasks, usage of GPU and CPU resources, and more. In fact, Ray itself has a powerful RL library that is called RLlib. Both Ray and RLlib have been increasingly adopted in academia and industry.

Info

For a comparison of Ray to other distributed backend frameworks such as Spark and Dask, see https://bit.ly/2T44AzK. You will see that Ray is a very competitive alternative, even beating Python's own multiprocessing implementation in some benchmarks.

Writing a production-grade distributed application...

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You're reading from Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

Table of Contents (24) Chapters

Implementing scalable deep Q-learning algorithms using Ray

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Implementing scalable deep Q-learning algorithms using Ray

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Table of Contents (24) Chapters