What does reinforcement learning focus on?

Reinforcement learning is a type of machine learning that emphasizes the processes of learning through reward and punishment. This framework mimics behavioral psychology concepts, where an agent learns to make decisions by taking actions in an environment and receiving feedback in the form of rewards or penalties. The goal is to maximize cumulative rewards over time by finding the most beneficial actions based on past experiences.

When an agent takes an action, it assesses the outcome and updates its understanding of which actions are preferable in various situations. This trial-and-error method allows reinforcement learning to adapt and improve as it encounters new scenarios. In contrast, the other options focus on different aspects of data handling and learning methodologies. For instance, finding data patterns pertains more to supervised and unsupervised learning, while learning through observation is a characteristic of imitation learning or observational learning. Extracting labeled data from large datasets is a function related to data preprocessing and labeling rather than the process of reinforcement learning itself.