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A partially observable Markov decision process (POMPD) is a Markov decision process in which the agent cannot directly observe the underlying states in the model. The Markov decision process (MDP) is a mathematical framework for modeling decisions showing a system with a series of states and providing actions to the decision maker based on those states.
The POMPD builds on that concept to show how a system can deal with the challenges of limited observation.
Techopedia Explains Partially Observable Markov Decision Process
In the partially observable Markov decision process, because the underlying states are not transparent to the agent, a concept called a “belief state” is helpful. The belief state provides a way to deal with the ambiguity inherent in the model.
The POMPD is useful in reinforcement learning where a system can go over the MPD or POMPD model utilizing what is known to build a clearer picture of probability outcomes.
