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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

Building the chatbot server

Python is a great framework and it provides a number of great tools for game development. However, we are going to focus on using Unity for our purposes. Unity is an excellent and very user-friendly game engine that will make setting up complex examples in later chapters a breeze. Don't worry if you don't know C#, the language of Unity, since we will be manipulating the engine through Python in many cases. This means we want the ability to run our Python code outside Unity and we want to do it on a server.

If you are developing your game in Python, using a server then becomes optional, except that there are very compelling reasons to set up your AI bots as services or microservices. Microservices are self-contained succinct applications or services that only interface through some form of well-known communication protocol. AI Microservices...

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