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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

Introducing Google Dopamine

Dopamine was developed at Google as a platform to showcase the company's latest advances in DRL. Of course, there are also other groups at Google doing the same thing, so it is perhaps a testament to how varied these platforms still are and need to be. In the next exercise, we will use Google Colab to build an example that uses Dopamine on the cloud to train an agent.

To access all of the features on Colab, you will likely need to create a Google account with payment authorized. This likely means entering a credit or debit card. The plus here is that Google provides $300 US in credits to use the GCP platform, of which Colab is one small part.

Open your browser to colab.research.google.com and follow the next exercise:

  1. We will first start by creating a new Python 3 Notebook. Be sure to choose this by the prompt dialog or through the Colab File...
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