A partially observable Markov decision process is a generalization of an MDP in which the agent cannot directly observe the full state of the environment. The agent must act using observations and beliefs about possible hidden states.
A generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a probability distribution over the set of possible states, based on a set of observations and observation probabilities, and the underlying MDP.