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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

Building a Unity environment

The ML-Agents toolkit provides not only a DRL training framework but also a mechanism to quickly and easily set up AI agents within a Unity game. Those agents can then be externally controlled through a Gym interface—yes, that same interface we used to train most of our previous agent/algorithms. One of the truly great things about this platform is that Unity provides several new demo environments that we can explore. Later, we will look at how to build our own environments for training agents.

The exercises in this section are meant to summarize the setup steps required to build an executable environment to train with Python. They are intended for newcomers to Unity who don't want to learn all about Unity to just build a training environment. If you encounter issues using these exercises, it is likely the SDK may have changed. If that...
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