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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

Building a Deep Learning Gaming Chatbot

Chatbots, or conversational agents, are an exploding trend in AI and are seen as the next human interface with the computer. From Siri, Alexa, and Google Home, there has been an explosion of commercial growth in this area, and you most likely already have interfaced with a computer in this manner. Therefore, it only seems natural that we cover how to build conversational agents for games. For our purposes, however, we are going to look at the class of bots called neural conversational agents. Their name follows from the fact that they are developed with neural networks. Now, chatbots don't have to just chat; we will also look at other ways conversational bots can be used in gaming.

In this chapter, we learn how to build neural conversational agents and how to apply these techniques to games. The following is a summary of the main topics...

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