While they share some similarities, Reinforcement learning and supervised learning differ in major ways.


Supervised learning is a type of machine learning where a model is trained using labelled data, which means data with a known label. The model is then used to make predictions on new, unlabelled data. It is a form of machine learning that is used for classifying data into different categories and for predicting outcomes.


On the other hand, reinforcement learning is a type of machine learning where the model is trained using rewards and punishments. This type of learning is often used for tasks that require a machine to take actions and make decisions. It is used to solve problems where the solution is not known, and the model has to learn from trial and error.


Supervised learning is used for classifying data and predicting outcomes, while reinforcement learning is used for problems where the solution is unknown and the machine must learn through trial and error.
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