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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
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

Debugging/Testing a Game with DRL

While the ML-Agents framework provides powerful capabilities for building AI agents for your games, it also provides automation for debugging and testing. The development of any complex software needs to be tied to extensive product testing and review by talented quality assurance teams. Testing every aspect, every possible combination, and every level can be extremely time-consuming and expensive. Therefore, in this chapter, we will look at using ML-Agents as an automated way to test a simple game. As we change or modify the game, our automated testing system can inform us of any issues or possible changes that may have broken the test. We can also take this further with ML-Agents, for instance, to evaluate training performance.

The following is a brief summary of what we will cover in this chapter:

  • Introducing the game
  • Setting up ML-Agents
  • ...
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