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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

Neural conversational agents

The concept of communicating with a computer via natural language first became popular as far back as Star Trek (1966 to 1969). In the series, we can often see Kirk, Scotty, and the gang issuing commands to the computer. Since then, many attempts have been made to build chatbots that can converse naturally with a human. During this often unsuccessful journey over the years, several linguistic methods have been developed. These methods are often grouped together and referred to as natural language processing, or NLP. Now, NLP still is the foundation for most chatbots, including the deep learning variety we will get to shortly.

We often group conversational agents by purpose or task. Currently, we categorize chatbots into two main types:

  • Goal-oriented: These bots are the kind Kirk would use or the ones you likely communicate with on a daily basis, and...
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