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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

Analyzing the testing process

One of the key features that ML-Agents is currently missing is extra training analytics (beyond what is provided by the console and TensorBoard). A key feature that could be crucial (and which is not difficult to add) is training analytics. This could be implemented with the Unity Analytics service that is free to try with all games. Since this isn't a current feature in ML-Agents, it is one that we will add in the next exercise, by adding our own training analytics system:

  1. Open the Unity editor, and from the menu, select Window | General | Services. This will open a new window called Services, usually over the top of the Inspector window.
  2. Click on the Analytics service in the newly opened Services window. You will need to progress through a couple of screens, asking for your preferences and acknowledgment, as shown in the following screenshot...
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