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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
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

Exercises

While your motivation may vary as to why you are reading this book, hopefully by now you can appreciate the value of just doing things on your own. As always, we present these exercises for your enjoyment and learning, and hope you have fun completing them:

  1. Select another sample scene that uses discrete actions and write the reward functions that go with it. Yes, that means you will need to open up and look at the code.
  2. Select a continuous action scene and try writing the reward functions for it. While this one may be difficult, it is essential if you want to build your own control training agent.
  3. Add Curriculum Learning to one of the other discrete action samples we have explored. Decide on how you can break the training into levels of difficulty and create parameters for controlling the evolution of the training.
  4. Add Curriculum Learning to a continuous action sample...
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