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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

Exercises

As always, try at least one or two of the following exercises on your own for your own enjoyment and learning:

  1. Open the BananaCollectors example Banana scene and run it in training mode.
  2. Modify the BananaCollectors | Banana scene so that it uses five separate learning brains and then run it in training mode.
  3. Modify the reward functions in the last SoccerTwos exercise to use exponential or logarithmic functions.
  4. Modify the reward function in the last SoccerTwos exercise to use non-inverse related and non-linear functions. This way, the mean modifying the positive and negative rewards is different for each personality.
  5. Modify the SoccerTwos scene with different characters and personalities. Model new rewards functions as well, and then train the agents.
  6. Modify the BananaCollectors example Banana scene to use the same personalities and custom reward functions as we did...
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