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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

Exercises

Use the exercises in this section to enhance and reinforce your learning. Attempt at least a few of these exercises on your own, and remember this is really for your benefit:

  1. Set up and run the 3DBall example environment to train a working agent. This environment uses multiple games/agents to train.
  2. Set the 3DBall example to let half of the games use an already trained brain and the other to use training or external learning.
  3. Train the PushBlock environment agents using external learning.
  4. Train the VisualPushBlock environment. Note how this example uses a visual camera to capture the environment state.
  5. Run the Hallway scene as a player and then train the scene using an external learning brain.
  6. Run the VisualHallway scene as a player and then train the scene using an external learning brain.
  7. Run the WallJump scene and then run it under training conditions. This example...
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