Abstract: A computer-implemented method for reducing computational costs for reducing computational costs to perform machine learning tasks includes generating one or more state partitioning candidates corresponding to a plurality of states associated with a partially observable Markov decision process (POMDP) model, determining that a given state partitioning candidate of the one or more state partitioning candidates satisfies a merge condition based on a state transition matrix for the given state partitioning candidate, and performing a machine learning task based on the POMDP model with merged states using the given state partitioning candidate.