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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

Exercises

As always, use the exercises in this section to get a better understanding of the material you learn. Try to work through at least two or three exercises in this section:

  1. Return to the example Chapter_5_1.py and change the alpha (learning_rate) variable and see what effect this has on the values calculated.
  2. Return to the example Chapter_5_2.py and alter the arm positions on the various bandits.
  3. Change the learning rate on the example Chapter_5_2.py and see what effect this has on the Q results output.

  1. Alter the gamma reward discount factor in the Chapter_5_3.py example, and see what effect this has on agent training.
  2. Change the exploration epsilon in the Chapter_5_3.py to different values and rerun the sample. See what effect altering the various exploration parameters has on training the agent.
  3. Alter the various parameters (exploration, alpha, and gamma) in the...
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