1. A method for learning a management policy, comprising: receiving a set of one or more exemplars, wherein at least two of the one or more exemplars comprise a (state, action) pair for a system, and at least one of the one or more exemplars comprises a reward responsive to performing an action in a state; obtaining a set of one or more pairwise distances between one or more pairs of the one or more exemplars; applying a nonlinear dimensionality reduction to the one or more exemplars and to the one or more pairwise distances in order to embed the one or more exemplars into a lower dimensional space, thereby producing embedded exemplars, wherein one or more parameters of the nonlinear dimensionality reduction are tuned to minimize a cross-validation Bellman error on a holdout set taken from the set of one or more exemplars; and applying a reward-based learning method to the embedded exemplars in order to derive a learned management policy, wherein at least one of: the receiving, the obtaining, the applying the nonlinear dimensionality reduction, or the applying the reward-based learning method is performed using a processor.