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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 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

Summary

In this chapter, we took a short tour of many basic concepts involving your next steps in DL and DRL; perhaps you will decide to pursue the Unity Obstacle Tower Challenge and complete that or just use DRL in your own project. We looked at simple quizzes in order to evaluate your potential for diving in and using DRL in a game. From there, we looked at the next steps in development, and then finally we looked at other areas of learning may want to focus on.

This book was an exercise in understanding how effective DL can be when applied to your game project in the future. We explored many areas of basic DL principles early on and looked at more specific network types such as CNN and LSTM. Then, we looked at how these basics network forms could be applied to applications for driving and building a chatbot. From there, we looked at the current king of machine learning algorithms...

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