Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, where the model is trained on a fixed dataset, RL involves learning from the consequences of actions in a dynamic environment.
