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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

Online training

Online Imitation Learning is where you teach the agent to learn the observations of a player or another agent in real time. It also is one of the most fun and engaging ways to train agents or bots. Let's jump in and set up the tennis environment for online Imitation Learning in the next exercise:

  1. Select the TennisArea | AgentA object and set Tennis Agent | Brain to TennisPlayer. In this IL scenario, we have one brain acting as a teacher, the player, and a second brain acting as the student, the learner.
  2. Select the AgentB object and make sure Tennis Agent | Brain is set to TennisLearning. This will be the student brain.
  3. Open the online_bc_config.yaml file from the ML-Agents/ml-agents/config folder. IL does not use the same configuration as PPO so the parameters will have similar names but may not respond to what you have become used to.
  4. Scroll down in the...
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