Markov Decision Processes

Markov Decision Processes (MDPs) are mathematical frameworks used to model decision-making in situations where outcomes are partly random and partly under the control of a decision maker. An MDP is defined by its states, actions, transition probabilities, and rewards. It provides a way to formalize the process of making a sequence of decisions to maximize some notion of cumulative reward. MDPs are widely used in various fields, including robotics, automated control, and economics, particularly where optimal decision-making is crucial under uncertainty.