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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

Questions

  1. What is the difference between a reward and a value?
  2. What is a hyperparameter? Give an example of a hyperparameter other than the ones discussed in this chapter.
  3. Why will a Q-learning agent not choose the highest Q-valued action for its current state?
  4. Explain one benefit of a decaying gamma.
  5. Describe in one or two sentences the difference between the decision-making strategies of SARSA and Q-learning.
  6. What kind of policy does Q-learning implicitly assume the agent is following?
  7. Under what circumstances will SARSA and Q-learning produce the same results?
You have been reading a chapter from
Hands-On Q-Learning with Python
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781789345803
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