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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

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

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

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Contextual bandits

We just saw how bandits are used for recommending the correct ad banner to the user. But the banner preference varies from user to user. User A likes banner type 1, but user B might like banner type 3. So we have to personalize ad banners according to user behavior. How can we do that? We introduce a new bandit type called contextual bandits.

In a normal MABs problem, we perform the action and receive a reward. But with contextual bandits, instead of just taking the actions alone, we take the environment state as well. The state holds the context. Here, the state specifies the user behaviors, so we will take actions (show ads) according to the state (user behavior) that will result in a maximum reward (ad clicks). Thus, contextual bandits are widely used for personalizing content according to the user's preference behavior. They are used to solve cold-start...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

See other products by Sudharsan Ravichandiran

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