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Hands-On Reinforcement Learning with Python

You're reading from   Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Length 318 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Reinforcement Learning FREE CHAPTER 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

Chapter 4

  1. The Monte Carlo algorithm is used in RL when the model of the environment is not known.
  2. Refer section Estimating the value of pi using Monte Carlo.
  3. In Monte Carlo prediction, we approximate the value function by taking the mean return instead of the expected return.
  4. In every visit Monte Carlo, we average the return every time the state is visited in an episode. But in the first visit MC method, we average the return only the first time the state is visited in an episode.

  1. Refer section Monte Carlo control.
  2. Refer section On-policy Monte Carlo control and Off-policy Monte Carlo control
  3. Refer section Let's play Blackjack with Monte Carlo.

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