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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

Installing ML-Agents

Installing Unity, the game engine itself, is not very difficult, but when working with ML-Agents, you need to be careful when you pick your version. As such, the next exercise is intended to be more configurable, meaning you may need to ask/answer questions while performing the exercise. We did this to make this exercise longer lasting since this toolkit has been known to change frequently with many breaking changes.

Unity will run on any major desktop computer (Windows, Mac, or Linux), so open your development computer and follow along with the next exercise to install Unity and the ML-Agents toolkit:

  1. Before installing Unity, check the ML-Agents GitHub installation page (https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) and confirm which version of Unity is currently supported. At the time of writing, this is 2017.4, and we...
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