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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 covered an emerging technique in RL called Imitation Learning or Behavioral Cloning. This technique, as we learned, takes the captured observations of a player playing a game and then uses those observations in an online or offline setting to further train the agent. We further learned that IL is just a form of Transfer Learning. We then covered a technique with ML-Agents that will allow you to transfer brains across environments. Finally, we looked at how to chain IL andtransfer learningas a way of stimulating the agent's training into developing new strategies on its own.

In the next chapter, we will further our understanding of DRL in games by looking at multiple agent training scenarios.

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