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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

Complete the following exercises in your own time and to improve your own learning experience. Improving your understanding of the material will make you a more successful deep learner, and you will likely enjoy this book better as well:

  1. In the Chapter_2_1.py example, change the Conv2D layers to use a different filter size. Run the sample again, and see what effect this has on training performance and accuracy.
  2. Comment out or delete a couple of the MaxPooling layers and corresponding UpSampling layers in the Chapter_2_1.py example. Remember, if you remove a pooling layer between layers 2 and 3, you likewise need to remove the up-sampling to remain consistent. Run the sample again, and see what effect this has on training time, accuracy, and performance.
  3. Alter the Conv2D layers in the Chapter_2_2.py example using a different filter size. See what effect this has on training...
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