In which type of machine learning does the algorithm learn from interactions with its environment, receiving feedback via rewards or penalties?

Reinforcement learning is a type of machine learning where an algorithm learns to make decisions by interacting with its environment. In this approach, the algorithm receives feedback in the form of rewards when it performs actions that lead to desirable outcomes and penalties for actions that result in negative outcomes. This trial-and-error method allows the algorithm to explore various strategies and optimize its behavior over time to maximize the accumulated rewards.

This learning paradigm is particularly powerful for problems where the correct action is not immediately clear and is determined based on a sequence of decisions. Reinforcement learning has been effectively applied in various domains, including robotics, gaming, and optimization tasks, where an agent must learn from its experience rather than relying solely on pre-labeled training data.