Reinforcement learning trains an agent through trial and error: it takes actions in an environment, receives rewards or penalties, and gradually learns a strategy that maximizes long-term reward. It is how a child learns to ride a bicycle, adjusting after every wobble. RL powers game-playing systems, robotics, and dynamic decision-making like ad bidding or energy management.