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Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximise some notion of cumulative reward. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Although each algorithm has advantages and limitations, no single algorithm works for all problems. In addition to performance bounds, learning theorists study the time complexity and feasibility of learning.