Reinforcement learning (RL) is a subset of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, where the model learns from a dataset with labeled examples, RL involves learning from the consequences of actions through trial and error.
