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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

A GAN for creating music

In our final grand example of this chapter, we are going to look at generating music with GANs for games. Music generation is not especially difficult, but it does allow us to see a whole variation of a GAN that uses LSTM layers to identify sequences and patterns in music. Then it attempts to build that music back from random noise to a passable sequence of notes and melodies. This sample becomes ethereal when you listen to those generated notes and realize the tune originates from a computer brain.

The origins of this sample are pulled from GitHub, https://github.com/megis7/musegen, and developed by Michalis Megisoglou. The reason we look at these code examples is so that we can see the best of what others have produced and learn from those. In some cases, these samples are close to the original, and others not so much. We did have to tweak a few things...

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