Buttfield-Addison P., Buttfield-Addison M., Nugent T., Manning J. Practical Simulations for Machine Learning

First Release — O’Reilly Media, Inc, 2022 - 484 p. — ISBN: 978-1-492-08992-6.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 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).Audience and Approach
We wrote this book for programmers and software engineers who are interested in machine learning but are not necessarily machine learning engineers. If you have a passing interest in machine learning or are starting to work more in the machine learning space, then this book is for you. If you’re a game developer, who kind of already knows Unity, or another game engine, and wants to learn machine learning (for either games or some other application) then this book is for you too.You'll learn how to:Design an approach for solving ML and AI problems using simulations with the Unity engine.
Use a game engine to synthesize images for use as training data.
Create simulation environments designed for training deep reinforcement learning and imitation learning models.
Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization.
Train a variety of ML models using different approaches.
Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits.