Supervised learning is a machine learning paradigm where the algorithm is trained on labeled data, where the desired output is known for each input. The goal of supervised learning is to learn the mapping between inputs and outputs, and to make predictions about the output for new, unseen inputs. Supervised learning algorithms are trained by minimizing the difference between the predicted outputs and the actual outputs.

Reinforcement learning, on the other hand, is a type of machine learning where the algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. In reinforcement learning, the algorithm does not receive labeled data, but instead receives feedback in the form of rewards or penalties based on the actions it takes. The goal of reinforcement learning is to learn a policy, which is a mapping from states to actions, that maximizes the cumulative reward over time. * One key difference between the two paradigms is that in supervised learning the desired output is known, while in reinforcement learning the desired outcome is not known, and the algorithm must learn it through trial and error.

* Another difference is that in supervised learning the algorithm is trained on a fixed dataset, while in reinforcement learning the algorithm must continually interact with its environment to receive feedback and improve its policy.

In summary, supervised learning is best suited for problems where the desired output is known for each input, and the goal is to learn the mapping between inputs and outputs. Reinforcement learning is best suited for problems where the goal is to learn a policy that maximizes a reward signal in an environment through trial and error.