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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

Understanding state

The Hallway and VisualHallway examples are essentially the same game problem, but provide a different perspective, or what we may refer to in reinforcement learning as environment or game state. In the Hallway example, the agent learns by sensor input, which is something we will look at shortly, while in the VisualHallway example, the agent learns by a camera or player view. What will be helpful at this point is to understand how each example handles state, and how we can modify it.

In the following exercise, we will modify the Hallway input state and see the results:

  1. Jump back into the Hallway scene with learning enabled as we left it at the end of the last exercise.
  2. We will need to modify a few lines of C# code, nothing very difficult, but it may be useful to install Visual Studio (Community or another version) as this will be our preferred editor. You...
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