Reinforcement learning is a machine learning paradigm where agents learn to make sequential decisions by interacting with environments, receiving feedback through rewards and penalties. Unlike supervised learning, RL agents discover optimal behaviors through trial-and-error exploration rather than labeled examples, making it ideal for autonomous decision-making in robotics, game playing, and resource optimization. The field balances a fundamental tension: agents must exploit known rewarding actions while simultaneously exploring new possibilities to discover better strategies—a tradeoff that defines every RL algorithm's character and effectiveness.