Markov decision process

A Markov decision process is a mathematical framework that helps scientists and engineers make optimal decisions when the outcome of each choice is uncertain but depends only on the current situation, not on how you got there. Think of it like a robot navigating a maze where each move leads to a new state with some probability, and the robot must choose actions that maximize its long-term reward, such as reaching the goal quickly. This approach is widely used in artificial intelligence, robotics, and operations research to solve real-world problems where you need to find the best strategy under conditions of uncertainty.