Monte Carlo POMDP
In the social class of Markov decision process algorithms, the Monte Carlo POMDP is the particle filter version for the partially observable Markov decision process algorithm. In MC-POMDP, particles filters are used to update and approximate the beliefs, and the algorithm is applicable to continuous valued states, actions, and measurements.