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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
2. Deep Learning for Games FREE CHAPTER 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

Introducing the game

The game that we are going to look at is a demo sample asset that is free and is an excellent example of a typical game. The game that we'll test will use discrete control mechanics and a first-person perspective, like the games that we have looked at in the past. The technique that we will show you here is how to map/hack into a game's controller so that it can be powered by ML-Agents. Using this technique should allow you to attach ML-Agents to any existing game, although different controllers, such as third-person or top-down, may require a slightly altered approach.

If you consider yourself an experienced Unity user and have your own project that uses an FPS system, then you should go ahead and try to adapt this sample to your own game or example.

You will generally find a lack of good sample game projects for Unity, due to a somewhat questionable...

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