Reinforcement Learning

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Reinforcement learning is a way to train an AI system by letting it learn through experience. The system takes an action, sees what happens, and receives feedback in the form of a reward or a penalty. Over time, it learns which actions lead to better outcomes. A simple example is a game-playing agent that earns points for winning. At first, it plays randomly, but as it gets feedback, it starts choosing moves that increase its chances of success.

This training style is useful when there is no clear “correct label” for each situation, and the system has to make a sequence of decisions. It’s often used in robotics, games, and control problems where actions affect what happens next. The challenge is that learning can be slow or unstable if rewards are sparse or poorly designed.

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