In a supervised learning model, the algorithm learns on a labeled data set, providing an answer key that the algorithm can use to evaluate its accuracy on training data. 
An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own.
Reinforcement learning trains an algorithm with a reward system, providing feedback when an artificial intelligence agent performs the best action in a particular situation.