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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 in this chapter are a mix of working with ML-Agents and building your own testing analysis platform. As such, choose one or two exercises that make sense for you to complete on your own from the following list:

  1. Configure the TestingAgent to use a different camera for its visual observation input.
  2. Enable Curiosity Learning on the agent's brain.
  3. Set up the TestingAgent to control a different vehicle.

  1. Set up the TestingAgent to run on another vehicle and let ML-Agents control both of the agents simultaneously.
  2. Add additional tracking analytics custom events for the agents. Perhaps track the distance that the agent travels versus its lifetime. This will provide a speed factor that can also denote the agent's efficiency. An agent that hits a goal quicker will have a better speed factor.
  3. Enable online imitation learning by adding a second vehicle...
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