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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

Imitation Transfer Learning

One of the problems with Imitation Learning is that it often focuses the agent down a path that limits its possible future moves. This isn't unlike you being shown the improper way to perform a task and then doing it that way, perhaps without thinking, only to find out later that there was a better way. Humanity, in fact, has been prone to this type of problem over and over again throughout history. Perhaps you learned as a child that swimming right after eating was dangerous, only to learn later in life through your own experimentation, or just common knowledge, that that was just a myth, a myth that was taken as fact for a very long time. Training an agent through observation is no different you limit the agent's vision in many ways to a narrow focus that is limited by what it was taught. However, there is a way to allow an agent to revert...

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