Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
Arrow right icon
View More author details
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

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
  • ...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image