Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards. Unlike supervised learning, RL does not rely on labeled input/output pairs. Instead, it learns through trial and error, receiving feedback from its actions and continually improving its strategy.