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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

Multi-brain play

One of the truly great things about the ML-Agents kit is the ability to add multiple agents powered by multiple brains quickly. This in turns gives us the ability to build more complex game environments or scenarios with fun agents/AI to play both with and against. Let's see how easy it is to convert our soccer example to let the agents all use individual brains:

  1. Open up the editor to the SoccerTwos scene we looked at earlier.
  2. Locate the Brains folder for the example at Assets | ML-Agents | Examples | Soccer | Brains.
  3. Click the Create menu in the upper right corner of the window and from the Context menu, and select ML-Agents | Learning Brain:
Creating a new learning brain
  1. Name the new brain RedStrikerLearning. Create three more new brains named RedGoalieLearning, BlueGoalieLearning, and BlueStrikerLearning in the same folder.
  1. Select RedStrikerLearning...
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