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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

Reinforcement learning

RL currently leads the pack in advances compared to other machine learning methodologies. Note the use of the word methodology and not technology. RL is a methodology or algorithm that applies a principle we can use with neural networks, whereas, neural networks are a machine learning technology that can be applied to several methodologies. Previously, we looked at other methodologies that blended with DL, but we focused more on the actual implementation. However, RL introduces a new methodology that requires us to understand more of the inner and outer workings before we understand how to apply it.

RL was popularized by Richard Sutton, a Canadian, and current professor at the University of Alberta. Sutton has also assisted in the development of RL at Google's DeepMind, and is quite often regarded as the father of RL.

At the heart of any machine learning...

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