Stochastic Control is a mathematical framework used to make decisions in uncertain environments. It combines elements of probability theory and control theory to optimize outcomes over time. This approach is particularly useful in fields like finance, robotics, and operations research, where randomness plays a significant role in system behavior. In Stochastic Control, decision-makers model systems that evolve randomly and seek to determine the best course of action at each stage. By analyzing potential future states and their probabilities, they can develop strategies that maximize expected rewards or minimize costs, adapting to new information as it becomes available.