Reinforcement Learning (RL) is a specialized area of machine learning that focuses on how agents can learn optimal behaviors through interactions with an environment. In RL, an agent takes actions based on its current state, receiving feedback in the form of rewards or penalties from the environment. The primary goal is to develop a policy that maximizes cumulative rewards over time. RL differs from supervised learning in that it does not rely on labeled input/output pairs but rather learns from trial and error. This approach is widely applied in various domains, including robotics, game playing (notably in AI systems like AlphaGo), and autonomous systems, where agents must navigate complex environments and make sequential decisions.