Reinforcement Learning (RL) is a method where an agent learns to navigate an environment by performing actions and receiving feedback in the form of rewards or penalties. This trial-and-error approach allows the agent to find the best strategy to achieve a goal, akin to training a pet to perform tricks using treats. It's related to supervised and unsupervised learning but uniquely focuses on decision-making processes over time.