Markov decision processes

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 (representing different situations), actions (choices made by the decision-maker), rewards (feedback from actions), and a transition model (how actions lead to new states). The key feature is that the future state depends only on the current state and action, not on past actions. MDPs are widely used in fields like robotics, economics, and artificial intelligence to find optimal strategies for decision-making.