Rewards and punishments in reinforcement learning can take a variety of forms, but they are typically numerical values that increase or decrease the likelihood of a certain action being taken. For example, if a reward is given when an action is taken, the model may increase its probability of taking that action in the future; whereas if a punishment is given, the model may decrease its probability of taking that action in the future.

The parameters of a function that handles the reinforcement component are typically adjusted based on the rewards and punishments the model receives. Additionally, feedback can be given to an AI model through various methods, such as providing the model with labeled examples, providing the model with feedback on its performance, or even providing the model with a reward or punishment for each action it takes.
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