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Reinforcement Learning (RL) is a subfield of machine learning where agents learn policies by trial-and-error and reward feedback to select actions. It models decision-making in sequential environments and suits control, optimization, and planning tasks. Use cases span robotics, game playing, and recommender or scheduling systems.
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