MDP (Markov Decision Process)

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 involves states representing different situations, actions that influence state transitions, and rewards received after actions. The process assumes the "Markov property," meaning the next state depends only on the current state and action, not on previous history. MDPs help in finding the best strategy or policy to maximize long-term rewards through systematic decision-making, often used in areas like robotics, finance, and AI planning.