Reinforcement Learning (RL) is a powerful branch of machine learning that enables intelligent agents to learn and make sequential decisions by interacting with an environment.
Unlike supervised learning, which relies on labeled data, and unsupervised learning, which explores unlabelled data, RL focuses on learning through trial and error, optimizing actions to maximize long-term rewards. In this article, we delve into the reasons why we use reinforcement learning and its unique capabilities in tackling complex decision-making problems.