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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.
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.