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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

Deep Learning for Games

Welcome to Hands-on Deep Learning for Games. This book is for anyone wanting an extremely practical approach to the complexity of deep learning (DL) for games. Importantly, the concepts discussed in this book aren't solely limited to games. Much of what we'll learn here will easily carry over to other applications/simulations.

Reinforcement learning (RL), which will be a core element we talk about in later chapters, is quickly becoming the dominant machine learning (ML) technology. It has been applied to everything from server optimization to predicting customer activity for retail markets. Our journey in this book will primarily be focused on game development, and our goal will be to build a working adventure game. Keep in the back of your mind how the same principles you discover in this book could be applied to other problems, such as simulations...

You have been reading a chapter from
Hands-On Deep Learning for Games
Published in: Mar 2019
Publisher: Packt
ISBN-13: 9781788994071
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