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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

Chapter 7, Decoupling Exploration and Exploitation in Multi-Armed Bandits

  1. The true probability distribution of an event is the actual likelihood of encountering that event. We discover this distribution through repeated experimental trials.
  2. Conducting more trials gives us a better picture of the true probability distribution of a problem. In most problem spaces, conducting 10 trials of an event would not give us sufficient data to develop a detailed model of the event.
  3. A small sample size might be biased in a way the experimenter is not aware of, and the descriptive statistics of that sample might not be reflected in a larger sample.
  4. Thompson sampling is a Bayesian method for optimization that involves choosing a prior probability distribution for an event and updating that as more information about the event is received. Since it is computationally expensive to try to find the...
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