Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by performing actions and receiving feedback from the environment. The goal is to maximize cumulative rewards over time. Unlike supervised learning, RL does not rely on a fixed dataset but learns through exploration and exploitation of the environment.
