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Hands-On Q-Learning with Python

You're reading from   Hands-On Q-Learning with Python Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345803
Length 212 pages
Edition 1st Edition
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Author (1):
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Nazia Habib Nazia Habib
Author Profile Icon Nazia Habib
Nazia Habib
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Q-Learning: A Roadmap FREE CHAPTER
2. Brushing Up on Reinforcement Learning Concepts 3. Getting Started with the Q-Learning Algorithm 4. Setting Up Your First Environment with OpenAI Gym 5. Teaching a Smartcab to Drive Using Q-Learning 6. Section 2: Building and Optimizing Q-Learning Agents
7. Building Q-Networks with TensorFlow 8. Digging Deeper into Deep Q-Networks with Keras and TensorFlow 9. Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym
10. Decoupling Exploration and Exploitation in Multi-Armed Bandits 11. Further Q-Learning Research and Future Projects 12. Assessments 13. Other Books You May Enjoy

Technical requirements

You will need the following packages installed to complete the exercises in this chapter and the upcoming chapters:

  • Python 3.5+ (we will be using Python 3.6 in this book)
  • NumPy
  • OpenAI Gym (see Chapter 3, Setting Up Your First Environment with OpenAI Gym, for installation and setup instructions)
We strongly encourage you to familiarize yourself with the official OpenAI Gym documentation for the Taxi-v2 environment as well as the other environments we will be working with in this book. You will find a great deal of useful information on these environments and how to access the information and functionality you need from them. You can find the documentation at https://gym.openai.com/docs/.

The code for the exercises in this chapter can be found at https://github.com/PacktPublishing/Hands-On-Q-Learning-with-Python/tree/master/Chapter04.

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