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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Chapter 17: Smart City and Cybersecurity

Smart cities are expected to be one of the defining experiences of the next decades. A smart city collects a lot of data using sensors located in various parts of the city, such as on the roads, utility infrastructures, and water resources. The data are then used to make data-driven and automated decisions, such as how to allocate the city resources, manage the traffic real-time, identify and mitigate infrastructure problems etc. This prospect comes with two challenges: How to program the automation and how to protect the highly-connected city assets from cyberattacks. Fortunately, reinforcement learning can help with both.

In this chapter, we cover three problems related to smart cities and cybersecurity and describe how to model them as RL problems. Along the way, we introduce you to the Flow library, a framework that connects traffic simulation software with RL libraries, and solve an example traffic light control problem.

In particular...