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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

Playing with Keras-RL

Keras is a very popular deep learning framework on its own and it is heavily used by newcomers looking to learn about the basics of constructing networks. The framework is considered very high-level and abstracts most of the inner details of constructing networks. It only goes to assume that an RL framework built with Keras would attempt to do the same thing.

This example is dependent on the version of Keras and TensorFlow and may not work correctly unless the two can work together. If you encounter trouble, try installing a different version of TensorFlow and try again.

To run this example, we will start by doing the installation and all of the setup in this exercise:

  1. To install Keras, you should create a new virtual environment using Python 3.6 and use pip to install it along with the keras-rl framework. The commands to do all of this on Anaconda are...
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