This book is a hands-on one, which means there are plenty of code examples to work through and discover on your own. The code for this book can be found in the following GitHub repository: https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-for-Games.
As such, be sure to have a working Python coding environment set up. Anaconda, which is a cross-platform wrapper framework for both Python and R, is the recommended platform to use for this book. We also recommend Visual Studio Code or Visual Studio Professional with the Python tools as good Integrated development editors, or IDEs.
Anaconda, recommended for this book, can be downloaded from https://www.anaconda.com/distribution/.
With that out of the way, we can move on to learning the basics of RL and, in the next section, look at why rewards-based learning works.
