Reinforcement Learning

Reinforcement learning trains a decision maker, called an agent, by letting it act in an environment and observing what happens. The agent takes an action, the environment returns a new situation and a reward, and the agent uses that experience to act better next time. Nobody hands the agent a labeled example of the right move.

That setup separates reinforcement learning from supervised learning. A supervised model learns from a fixed dataset of correct answers. An agent learns from its own trial and error, and its own choices change what data it sees next.

What it is for

Use reinforcement learning when the right behavior is a sequence of decisions, each one shaping the situations that follow, and when you can define a reward signal for good outcomes. Common use cases include: