What does reinforcement learning primarily involve?

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The core idea revolves around the agent taking actions and receiving feedback in the form of rewards or penalties based on those actions. This feedback system encourages the agent to learn optimal behaviors by maximizing cumulative rewards over time.

In this context, the agent explores different strategies and learns from the consequences of its actions, adjusting its approach based on the received feedback. The continuous loop of action, feedback, and learning is what sets reinforcement learning apart from other types of machine learning, such as supervised learning, where models are trained using labeled data, or unsupervised learning, which does not involve direct feedback mechanisms.

In summary, reinforcement learning emphasizes the importance of interaction and learning from the consequences of actions, making it distinct from approaches that rely solely on pre-existing labels or structured data.