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Hands-On Reinforcement Learning with Python

You're reading from  Hands-On Reinforcement Learning with Python

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Sudharsan Ravichandiran Sudharsan Ravichandiran
Profile icon Sudharsan Ravichandiran

Table of Contents (16) Chapters

Preface 1. Introduction to Reinforcement Learning 2. Getting Started with OpenAI and TensorFlow 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Deep Learning Fundamentals 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Capstone Project – Car Racing Using DQN 13. Recent Advancements and Next Steps 14. Assessments 15. Other Books You May Enjoy

Questions

The question list is as follows:

  1. How does TD learning differ from the Monte Carlo method?
  2. What exactly is a TD error?
  3. What is the difference between TD prediction and control?
  4. How to build an intelligent agent using Q learning?
  5. What is the difference between Q learning and SARSA?
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