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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 2. Getting Started with OpenAI and TensorFlow FREE CHAPTER 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 6

  1. An MAB is actually a slot machine, a gambling game played in a casino where you pull the arm (lever) and get a payout (reward) based on a randomly generated probability distribution. A single slot machine is called a one-armed bandit and, when there are multiple slot machines it is called multi-armed bandits or k-armed bandits.
  2. An explore-exploit dilemma arises when the agent is not sure whether to explore new actions or exploit the best action using the previous experience.
  3. The epsilon is used to for deciding whether the agent should explore or exploit actions with 1-epsilon we choose best action and with epsilon we explore new action.
  4. We can solve explore-exploit dilemma using a various algorithm such epsilon-greedy policy, softmax exploration, UCB, Thompson sampling.
  5. The UCB algorithm helps us in selecting the best arm based on a confidence interval.
  6. In Thomson sampling...
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