Lanham - Hands-on deep learning for games: leverage the power of neural networks and reinforcement learning to build intelligent games

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First published: March 2019

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ISBN 978-1-78899-407-1

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Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

This book would not be possible if it wasn't for the researchers and contributors. This book has been built on top of, including the development of the ML-Agents toolkit by Unity Technologies, with both Dr. Danny Lange and Dr. Arthur Juliani taking a leading role. This book would also not be possible without the support of my family, friends, Rhonda and my co-workers. I'd like to give a special thanks to those who attend my deep learning tutorials and have given additional feedback.

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As we enter the 21st century, it is quickly becoming apparent that AI and machine learning technologies will radically change the way we live our lives in the future. We now experience AI daily, from conversational assistants to smart recommendations in a search engine, and the average user/consumer now expects a smarter interface in anything they do. This most certainly includes games, and is likely one of the reasons why you, as a game developer, are considering reading this book.

This book will provide you, with a hands-on approach to building deep learning models for simple encoding for the purpose of building self-driving algorithms, generating music, and creating conversational bots, finishing with an in-depth discovery of deep reinforcement learning (DRL). We will begin with the basics of reinforcement learning (RL) and progress to combining DL and RL in order to create DRL. Then, we will take an in-depth look at ways to optimize reinforcement learning to train agents in order to perform complex tasks, from navigating hallways to playing soccer against zombies. Along the way, we will learn the nuances of tuning hyperparameters through hands-on trial and error, as well as how to use cutting-edge algorithms, including curiosity learning, Curriculum Learning, backplay, and i mitation learning, in order to optimize agent training.

This book is for any gameor buddinggame developer who is interested in using deep learning in an aspect of their next game project. In order to be successful in learning this material, you should have knowledge of the Python programming language and another C-based language, such as C#, C, C++, or Java. In addition, a basic knowledge of calculus, statistics, and probability will aid your digestion of the materials and facilitate your learning, but this is not essential.

, Deep Learning for Games , covers the background of deep learning in games before moving on to cover the basics by building a basic perceptron. From there, we will learn the concepts of network layers and build a simple autoencoder.