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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

IL, or behavioral cloning

IL, or behavioral cloning, is the process by which observations and actions are captured from a human, or perhaps another AI, and used as input into training an agent. The agent essentially becomes guided by the human and learns by their actions and observations. A set of learning observations can be received by real-time play (online) or be extracted from saved games (offline). This provides the ability to capture play from multiple agents and train them in tandem or individually. IL provides the ability to train or, in effect, program agents for tasks you may find impossible to train for using regular RL, and because of this, it will likely become a key RL technique that we use for most tasks in the near future.

It is hard to gauge the value something gives you until you see what things are like without it. With that in mind, we will first start by...

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