Which of the following is an example of reinforcement learning?

Reinforcement learning is a unique area of machine learning characterized by an agent learning to make decisions by performing actions in an environment to maximize some notion of cumulative reward. The essence of reinforcement learning is the interaction between the agent and the environment, where the agent receives feedback in the form of rewards or penalties based on its actions. This cyclical process allows the agent to learn the best actions to take in various situations through trial and error.

In this context, the choice that describes learning through interaction and feedback captures the fundamental mechanism of reinforcement learning. The focus is on the improvement of the agent's strategy over time based on the feedback it receives, which aligns precisely with the principles of reinforcement learning.

On the other hand, generating predictions from historical data pertains more to supervised learning, where a model learns from labeled examples and does not depend on real-time feedback from an environment. Finding patterns in unlabeled data is a characteristic of unsupervised learning, where the goal is to discover inherent structures without feedback. Creating new visualizations based on past data typically falls under data visualization or exploratory data analysis rather than learning by interacting with an environment.