Reinforcement learning is a type of machine learning in which an agent learns to make decisions by interacting with an environment. The agent takes actions, receives feedback in the form of rewards or penalties, and adjusts its behavior over time to maximize cumulative reward. It is often modeled mathematically using concepts such as states, actions, and reward functions, and is widely used in areas like robotics, game playing, and control systems.