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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

Training an agent

For much of this book, we have spent our time looking at code and the inner depths of deep learning (DL) and reinforcement learning (RL). With that knowledge established, we can now jump in and look at examples where deep reinforcement learning (DRL) is put to use. Fortunately, the new agent's toolkit provides several examples to demonstrate the power of the engine. Open up Unity or the Unity Hub and follow these steps:

  1. Click the Open project button at the top of the Project dialog.
  2. Locate and open the UnitySDK project folder as shown in the following screenshot:
Opening the UnitySDK project
  1. Wait for the project to load and then open the Project window at the bottom of the editor. If you are asked to update the project, just be sure to say yes or continue. Thus far, all of the agent code has been designed to be backward compatible.

  1. Locate and open...
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