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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

Questions

  1. What is the true probability distribution of an event? How can we discover what this distribution is?
  2. Why is it more useful to conduct 1,000 trials of an experiment than 10 trials? Give a general explanation in 2–3 sentences.
  3. Explain the importance of having a large enough sample size when conducting an experiment.
  4. Explain what Thompson sampling is in 2–3 sentences.
  5. Explain how the Bayesian control rule relates to Thompson sampling.
  6. Briefly explain the difference between A/B testing and MAB testing.
  7. Why are user ad-clicks a useful demonstration of this type of testing methodology? What might another good example problem be?

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