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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

Obstacle Tower Challenge and Beyond

In this chapter, our final one, we will take a look at the current and future state of deep learning (DL) and deep reinforcement learning (DRL) for games. We take an honest and candid look to see whether these technologies are ready for prime-time commercial games or whether they are just novelties. Are we poised to see DRL agents beating human players at every game imaginable a few years from now? While that remains to be seen, and things are changing quickly, the question really is this: is DL ready for your game? It likely is a question you are asking yourself at this very moment, and it is hopefully one we will answer in this chapter.

This chapter will be a mix of hands-on exercises and general discussions with unfortunately no exercises. Well, there is one big exercise, but we will get to that shortly. Here is what we will cover in this...

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