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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

The exercises at the end of this chapter could likely provide several hours of fun. Try and only complete one or two exercises, as we still need to finish the book:

  1. Set up and run the PyramidsIL scene to run online IL.
  2. Set up and run the PushBlockIL scene to run online IL.
  3. Set up and run the WallJump scene to run with online IL. This requires you to modify the scene.
  4. Set up and run the VisualPyramids scene to use offline recording. Record a training session then train an agent.
  5. Set up and run the VisualPushBlock scene to use offline recording. Use offline IL to train the agent.
  6. Set up the PushBlockIL scene to record an observation demo. Then use this offline training to train multiple agents in the regular PushBlock scene.
  7. Set up the PyramidsIL scene to record a demo recording. Then use this for offline training to train multiple agents in the regular Pyramids scene...
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