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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 difference between an extensional and an intensional definition?
  2. Define the concept of feedforward in a neural network.
  3. Explain what role the weights play in a neural network. How is an input value propagated through the network?
  4. Briefly describe gradient descent.
  5. Briefly describe backpropagation.
  6. Describe the difference between a policy agent and a value agent.
  7. What is the difference between a tensor and an array? What benefit do we get from using tensors?
  8. What is a placeholder tensor?
  9. How does a Q-network update its internal approximation of the Q-values of a state-action function?
  10. What types of architectures qualify as deep Q-networks?
  11. Briefly describe the difference between a neural network that implements a Q-learning algorithm and a deep Q-network.
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