Hands-On Reinforcement Learning with Python

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: ...