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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

Testing through imitation

At this point in your learning, you have learned several strategies that we can apply to help our testing agent learn and find the goals. We can use curiosity or curriculum learning fairly easily, and we will leave that as an exercise for the reader. What we want is a way to control some of the testing process, and we don't really want our agent to randomly test everything (at least not at this stage). Sure, there are places where completely random testing works well. (By the way, this random form of testing is called monkey testing, because it resembles a monkey just mashing keys or input.) However, in a space such as our game, exploring every possible combination could take a very long time. Therefore, the best alternative is to capture player recordings and use them for our testing agent as a source for imitation learning.

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