Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-net

The topic of this book is Reinforcement Learning—which is a subfield of Machine Learning—focusing on the general and challenging problem of learning optimal behavior in complex environment. The learning process is driven only by reward value and observations obtained from the environment. This model is very general and can be applied to many practical situations from playing games to optimizing complex manufacture processes. Due to flexibility and generality, the field of Reinforcement Learning is developing very quickly and attracts lots of attention both from researchers trying to improve existing or create new methods, as well as from practitioners interested in solving their problems in the most efficient way. This book was written as an attempt to fill the obvious lack of practical and structured information about Reinforcement Learning methods and approaches. On one hand, there are lots of research activity all around the world, new research papers are being published almost every day, and a large portion of Deep Learning conferences such as NIPS or ICLR is dedicated to RL methods. There are several large research groups focusing on RL methods application in Robotics, Medicine, multi-agent systems, and others. The information about the recent research is widely available, but is too specialized and abstract to be understandable without serious efforts. Even worse is the situation with the practical aspect of RL application, as it is not always obvious how to make a step from the abstract method described in the mathematical-heavy form in a research paper to a working implementation solving actual problem. This makes it hard for somebody interested in the field to get an intuitive understanding of methods and ideas behind papers and conference talks. There are some very good blog posts about various RL aspects illustrated with working examples,