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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 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

Learning to tune PPO

In this section, we are going to learn to tune a modified/new control learning environment. This will allow us to learn more about some inner workings of the Unity example, but will also show you how to modify a new or modified sample on your own later. Let's begin by opening up the Unity editor so we can complete the following exercise:

  1. Open the Reacher scene, set it for learning, and run it in training. You should be able to do this part in your sleep now. Let the agent train for a substantial amount of time so you can establish a baseline, as always.
  2. From the menu, select Assets/Import Package/Custom Package. Locate Chapter_8_Assets.unitypackage from the Chapter08 folder of the books downloaded to the source code.
  3. Open up the Reacher_3_joint scene from the Assets/HoDLG/Scenes folder. This is the modified scene, but we will go through its construction...
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