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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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Toc

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

Setting up ML-Agents

At the time of writing this book, ML-Agents is developed and shipped as a GitHub project. It is likely that as the product matures, it will be shipped as its own asset package, but currently, it is not.

Therefore, we first need to export ML-Agents as an asset package. Open up a new Unity Editor session to an ML-Agents or Unity SDK project, and follow these steps:

  1. Locate the ML-Agents folder in the Project window, and select it.
  2. From the menu, select Assets | Export Package.
  3. Be sure that all of the folder contents are highlighted, as shown in the following Exporting package dialog excerpt:
Exporting ML-Agents as an asset package
  1. Be sure to uncheck the Include dependencies checkbox, as shown in the preceding excerpt. As long as you have the proper root folder selected, all of the dependencies that we need should get packaged.
  2. Click on the Export... button...
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