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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
2. Brushing Up on Reinforcement Learning Concepts FREE CHAPTER 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 6, Digging Deeper into Deep Q-Networks with Keras and TensorFlow

  1. Keras abstracts many of the functionalities provided by TensorFlow and creates a high-level frontend for creating complex deep learning architectures.
  2. CartPole is effectively a binary prediction problem because there are two options provided for every action taken.
  3. When the state space is very large, some states can be grouped together and treated similarly when the optimal actions to take from those states are the same.
  1. Experience Replay updates the Q-function using samples of past actions rather than updating it after every action. This helps prevent overfitting by smoothing away outlier actions and having the agent forget previous experiences in favor of new ones.
  2. An RL model approximating Q-values does not know what those actual Q-values are and progressively develops estimates for those values. A...
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