To get the most out of this book
This book is suitable for you if you’re using a machine with at least 32 GB of RAM. A GPU is not strictly required, but an Nvidia GPU is highly recommended. The code has been tested on Linux and macOS. For more details on the hardware and software requirements, refer to Chapter 2.
All the chapters in this book that describe RL methods have the same structure: in the beginning, we discuss the motivation of the method, its theoretical foundation, and the idea behind it. Then, we follow several examples of the method applied to different environments with the full source code.
You can use the book in different ways:
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To quickly become familiar with a particular method, you can read only the introductory part of the relevant chapter
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To get a deeper understanding of the way the method is implemented, you can read the code and the explanations accompanying it
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To gain a deeper familiarity with the method (which I beleive is the best way to learn) you can try to reimplement the method and make it work, using the provided source code as a reference point
Whichever approach you choose, I hope the book will be useful for you!
