Reinforcement Learning (RL) is a subfield 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 a continuous interaction between the agent and the environment, allowing for dynamic learning.
