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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

Sparsity of rewards

We call the situation where an agent does not get enough, or any, positive rewards, a sparsity of rewards. The simplest way to show how a sparsity of rewards can happen is by example, and fortunately, the GridWorld example can easily demonstrate this for us. Open the editor to the GridWorld example and follow this exercise:

  1. Open the GridWorld sample scene from where we left it in the last exercise. For the purposes of this exercise, it is also helpful to have trained the original sample to completion. GridWorld is one of those nice compact examples that train quickly and is an excellent place to test basic concepts, or even hyperparameters.
  1. Select the GridAcademy and change the Grid Academy | Reset Parameters | gridSize to 25, as shown in the following screen excerpt:
Setting the GridAcademy gridSize parameter
  1. Save the scene and the project.
  2. Launch the...
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