In this article, we will discuss reinforcement learning in Click-Through-Rate (CTR) prediction of web advertisements. We will see the practical implementation of Upper Confidence Bound (UCB), a method of reinforcement learning applied in this task. Using this implementation, one can be able to find the best version of the advertisement from a set of available versions that can get a maximum number of clicks by the visitors on the website.

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