Reinforcement Learning is a branch of Machine Learning, also called Online Learning. It is used to solve interacting problems where the data observed up to time t is considered to decide which action to take at time t + 1. It is also used for Artificial Intelligence when training machines to perform tasks such as walking. Desired outcomes provide the AI with reward, undesired with punishment. Machines learn through trial and error.
Reinforcement Learning is learning how to act in order to maximize a numerical reward.
In this part, you will understand and learn how to implement the following Reinforcement Learning models:
Upper Confidence Bound (UCB)
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