Using this [[algorithm]], the [[machine]] is trained to make specific [[decision]]s. It works this way: the machine is exposed to an [[environment]] where it trains itself continually using [[trial]] and [[error]]. This machine learns from past [[experience]] and tries to capture the best possible [[knowledge]] to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process.
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Machine learning [[algorithm]]s can be divided into 3 broad categories — [[supervised learning]], [[unsupervised learning]], and [[reinforcement learning]]. Supervised learning is useful in cases where a property (label) is available for a certain [[dataset]] (training set), but is missing and needs to be predicted for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message.