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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 RL equivalent of a labeled training dataset in supervised learning?
  2. What is one of the difficulties of not having a standardized set of environments for developing RL algorithms? How does Gym attempt to solve this problem?
  3. What is the difference between an actuated joint and an unactuated joint?
  4. What is the benefit of being able to use a single algorithm to solve more than one environment? Explain in two to three sentences.
  5. What is the importance of being able to solve generalized control problems in robotics motion?
  6. Briefly describe the relationship between a probability distribution and our current estimation of the likelihood of an event.
  1. Explain what difference it makes to have a state space available in a contextual bandit problem.
  2. Describe the differences in the results of A/B testing versus multi-armed bandit testing in two to three sentences.
  3. ...
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