Partially Observable Markov Decision Process (POMDP)

A Partially Observable Markov Decision Process (POMDP) is a framework used for decision-making where an agent must act in an uncertain environment. Unlike systems where the complete state is known, in a POMDP, the agent only receives limited, noisy information about the true situation. It maintains a belief — an educated guess — about the current state based on past actions and observations. The goal is to choose actions that maximize expected rewards over time, considering the uncertainty. POMDPs are useful in complex, real-world problems like robotics, healthcare, and autonomous systems, where full clarity of the environment isn't always possible.