This chapter introduces the Python libraries that we can use in order to implement the neuroevolution algorithms we described in the previous chapter. We will also discuss the strengths and weaknesses of each library that's presented. In addition to this, we will provide basic usage examples. Then, we will consider how to set up the environment for the experiments that we will perform later in this book and examine common ways to do this in the Python ecosystem. Finally, we will demonstrate how to set up a working environment using Anaconda Distribution, which is a popular tool for managing Python dependencies and virtual environments among data scientists. In this chapter, you will learn how to start using Python to experiment with the neuroevolution algorithms that will be covered in this book.
In this chapter, we will cover the following...
