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Hands-On Reinforcement Learning for Games

You're reading from   Hands-On Reinforcement Learning for Games Implementing self-learning agents in games using artificial intelligence techniques

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839214936
Length 432 pages
Edition 1st Edition
Languages
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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 (19) Chapters Close

Preface 1. Section 1: Exploring the Environment
2. Understanding Rewards-Based Learning FREE CHAPTER 3. Dynamic Programming and the Bellman Equation 4. Monte Carlo Methods 5. Temporal Difference Learning 6. Exploring SARSA 7. Section 2: Exploiting the Knowledge
8. Going Deep with DQN 9. Going Deeper with DDQN 10. Policy Gradient Methods 11. Optimizing for Continuous Control 12. All about Rainbow DQN 13. Exploiting ML-Agents 14. DRL Frameworks 15. Section 3: Reward Yourself
16. 3D Worlds 17. From DRL to AGI 18. Other Books You May Enjoy

Training a visual agent

Unity develops a 2D and 3D gaming engine/platform that has become the most popular platform for building games. Most of these games are the 3D variety, hence the specialized interest by Unity in mastering the task of agents that can tackle more 3D natural worlds. It naturally follows then that Unity has invested substantially into this problem and has/is working with DeepMind to develop this further. How this collaboration turns out remains to be seen, but one thing is for certain is that Unity will be our go-to platform for exploring 3D agent training.

In the next exercise, we are going to jump back into Unity and look at how we can train an agent in a visual 3D environment. Unity is arguably the best place to set up and build these type of environments as we have seen in the earlier chapters. Open the Unity editor and follow these steps:

  1. Open the VisualHallway...
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