A Markov Decision Process (MDP) is a mathematical framework used for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. An MDP is defined by a set of states, actions, transition probabilities, and rewards. The goal is to find a policy that maximizes the expected cumulative reward over time. In an MDP, the Markov property ensures that the future state depends only on the current state and action, not on past states. This property simplifies the decision-making process, allowing for efficient algorithms to determine optimal policies, such as dynamic programming and reinforcement learning.