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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

The past, present, and future of DL

While the term deep learning was first associated with neural networks in 2000 by Igor Aizenberg and colleagues, it has only become popular in the last 5 years. Prior to this, we called this type of algorithm an artificial neural network (ANN). However, deep learning refers to something broader than ANNs and includes many other areas of connected machines. Therefore, to clarify, we will be discussing the ANN form of DL for much of the remainder of this book. However, we will also discuss some other forms of DL that can be used in games, in Chapter 5, Introducing DRL.

The past

The first form of a multilayer perceptron (MLP) network, or what we now call an ANN, was introduced by Alexey Ivakhnenko...

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