Practical Simulations for Machine Learning

Preface

Welcome to Practical Simulations for Machine Learning! This book combines two of our favorite things: video game engines and artificial intelligence. We hope you enjoy reading it as much as we enjoyed writing it.

Specifically, this book explores the use of Unity, a product that used to be called a game engine but now likes to be called a platform for creating and operating interactive, real-time 3D content. That’s a lot of words, but they basically boil down to this: Unity is a platform for building things in 3D, and though it has traditionally been used for video game development, it can be used to build anything that can be represented in 3D, by using a combination of 3D graphics, physics simulations, and inputs of some kind.

By combining a platform for creating and operating interactive, real-time 3D content with machine learning tools, you can use the 3D world you create to train a machine learning model, kind of like it’s the real world. It’s not actually like the real world, but it’s fun to imagine, and there are some legitimately useful connections to the real world (such as being able to generate both data for use in real-world machine learning applications, as well as models that can be transposed to physical, real-world objects, like robots).

Tip

When we say real-world, we actually mean physical.

Combining Unity with machine learning is a great way to create both simulations and synthetic data, which are the two different topics we cover in this book.

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