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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
Toc

Table of Contents (16) Chapters close

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

Applications of MAB

So far, we have looked at the MAB problem and how we can solve it using various exploration strategies. But bandits are not just used for playing slot machines; they have many applications.

Bandits are used as a replacement for AB testing. AB testing is one of the commonly used classical methods of testing. Say you have two versions of the landing page of your website. How do you know which version is liked by most of the users? You conduct an AB test to understand which version is most liked by users.

In AB testing, we allocate a separate time for exploration and a separate time for exploitation. That is, it has two different dedicated periods only for exploration and exploitation alone. But the problem with this method is that this will incur a lot of regrets. So, we can minimize the regret using various exploration strategies that we use to solve MAB. Instead...

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