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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

Leave a review - let other readers know what you think

Identifying the right advertisement banner using MAB

Let us say you are running a website and you have five different banners for the same ad, and you want to know which banner attracts the user. We model this problem statement as a bandit problem. Let us say these five banners are the five arms of the bandit and we award one point if the user clicks the ad and award zero if the user does not click the ad.

In normal A/B testing, we will perform a complete exploration of all these five banners before deciding which banner is the best. But that will cost us a lot of energy and time. Instead, we will use a good exploration strategy for deciding which banner will give us the most rewards (most clicks).

First, let us import the necessary libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Let us simulate a dataset...

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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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