Reinforcement Learning allows AI to learn through trial and error, optimizing actions for rewards.

Unlike supervised learning which uses labeled data, RL agents interact with an environment, receiving rewards or penalties for their actions, and learn a policy to maximize cumulative reward over time. This is key for game playing and robotics.