April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Solving a multi-armed bandit problem in Python – user advertisement clicks
In this section we'll be solving a multi-armed bandit problem using a simulated set of ad-click data. We'll generate a set of clicks for 5 different advertisements. Each ad will either be clicked or not clicked when it is shown to a user. Our goal is to determine which ad to show next based on how each ad is performing at any given point in the simulation.
We start with a baseline loop that chooses a random advertisement from the selection each time. This model does not learn from its actions and always chooses a random action. If the user clicks on the ad, we get a reward of 1; if not, we get a reward of 0.
We import the necessary packages and generate a data frame ...
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