Practical Reinforcement Learning

This is the code repository for Practical Reinforcement Learning, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element.

This book aims to strengthen your machine learning skills by acquainting you with reinforcement learning algorithms and techniques. This book is divided into three parts. The first part defines Reinforcement Learning and describes its basics. It also covers the basics of Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms via practical applications.

By the end of this book, you'll know the practical implementation of case studies and current research activities to help you advance further with Reinforcement Learning.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

for _ in range(1000):
 env.render()
 env.step(env.action_space.sample())

This book covers all the practical examples in Python and Java. You need to install Python 2.7 or Python 3.6 in your computer. If you are working on Java, then you have to install Java 8. All the other reinforcement-learning-related toolkits or framework installations will be covered in the relevant sections.

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