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Hands-On Deep Learning for Games

You're reading from   Hands-On Deep Learning for Games Leverage the power of neural networks and reinforcement learning to build intelligent games

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics FREE CHAPTER
2. Deep Learning for Games 3. Convolutional and Recurrent Networks 4. GAN for Games 5. Building a Deep Learning Gaming Chatbot 6. Section 2: Deep Reinforcement Learning
7. Introducing DRL 8. Unity ML-Agents 9. Agent and the Environment 10. Understanding PPO 11. Rewards and Reinforcement Learning 12. Imitation and Transfer Learning 13. Building Multi-Agent Environments 14. Section 3: Building Games
15. Debugging/Testing a Game with DRL 16. Obstacle Tower Challenge and Beyond 17. Other Books You May Enjoy

Overriding the Unity input system

One of Unity's most compelling features is its ability to be cross-platform across any system, and with that comes several helpful layers of abstraction that we can use to inject our code into. However, the game in question needs to be following the Unity best practices in order to make this injection easy. That isn't to say that we couldn't do it by overriding the game's input system; it just wouldn't be as easy.

Before we get into describing how the injection works, let's take a step back and look at the best practices for using the Unity input system. Over the years, the Unity input system has evolved from a simple query that the device uses for inputs to the more cross-platform system that it uses now. However, many developers, including Unity itself, still use input methods that query a particular key code...

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