Reinforcement learning (RL) is an area of machine learning (ML) concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Reinforcement learning differs from supervised learning in not needing clean labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

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