This book was written as an attempt to fill the obvious gap in practical and structured information about RL methods and approaches. On the one hand, there is lots of research activity all around the world. New research papers are being published almost every day, and a large portion of deep learning (DL) conferences, such as Neural Information Processing Systems (NeurIPS) or the International Conference on Learning Representations (ICLR), are dedicated to RL methods. There are also several large research groups focusing on the application of RL methods to robotics, medicine, multi-agent systems, and others.

Information about the recent research is widely available, but it is too specialized and abstract to be easily understandable. Even worse is the situation surrounding the practical aspect of RL, as it is not always obvious how to make the step from an abstract method described in its mathematical-heavy form in a research paper to a working implementation solving an actual problem.

This makes it hard for somebody interested in the field to get a clear understanding of the methods and ideas behind papers and conference talks. There are some very good blog posts about various RL aspects that are illustrated with working examples, but the limited format of a blog post allows authors to describe only one or two methods, without building a complete structured picture and showing how different methods are related to each other. This book is my attempt to address this issue.