Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
Index

Why I wrote this book

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.