Partially observable Markov decision process

A **Partially Observable Markov Decision Process** (POMDP) is a framework used in decision-making problems where an agent operates in an environment that is partially observable and stochastic. It generalizes the Markov Decision Process (MDP) to situations where the agent cannot directly observe the state of the environment, making it a powerful model for a variety of applications such as robotics, artificial intelligence, and economics.