Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

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...