A Markov Decision Process (MDP) is a mathematical framework used to model decision-making situations where outcomes are partly random and partly under the control of a decision-maker. It consists of states, actions, transition probabilities, and rewards. In an MDP, the decision-maker chooses actions based on the current state, and the system transitions to a new state according to defined probabilities, receiving rewards based on the actions taken. MDPs are widely used in various fields, including artificial intelligence, robotics, and economics. They help in developing strategies for optimal decision-making over time, allowing systems to learn and adapt, such as in reinforcement learning algorithms like Q-learning and Deep Q-Networks.