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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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Toc

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 your game

Now that you have decided to use deep learning and/or deep reinforcement learning for your game, it is time to determine how you plan to implement various functionality in your game. In order to do that, we are going to go through a table outlining the steps you need to go through in order to build your game's AI agent:

Step

Action

Summary

Start Determine at what level you want the AI in the game to operate, from basic, perhaps for just testing and simple automation, to advanced, where the AI will complete against the player. Determine the level of AI.
Resourcing Determine the amount of resources. Basic AI or automation could be handled within the team itself, whereas more complex AI may require one or many experienced members of staff. Team requirements.
Knowledge Determine the level of knowledge the team possesses and what will be required...
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