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. It consists of states, actions, transition probabilities, and rewards. The decision-maker chooses actions to maximize cumulative rewards over time, considering the likelihood of moving between states. MDPs are widely used in various fields, including robotics, economics, and artificial intelligence. They help in developing strategies for optimal decision-making by evaluating the consequences of actions in uncertain environments. The key property of MDPs is the Markov property, which states that future states depend only on the current state and action, not on past states.