1. A machine-implemented method for selecting up to a predetermined number of a plurality of interaction policies for use on a wireless communication device, the method comprising: providing sets of values for a plurality of parameters of a plurality of user models, the plurality of user models corresponding to expected users of the wireless communication device, each of the plurality of parameters representing at least one of a user preference or contextual information with respect to use of the processing device; learning which ones of the plurality of interaction policies are within a specific tolerance of an optimal interaction policy for the plurality of user models, wherein the learning of which of the ones of the plurality of interaction policies are within the specific tolerance of the optimal interaction policy for the plurality of user models further comprises: calculating an average score of each of the plurality of interaction policies with respect to each of the plurality of user models; and determining which ones of the plurality of interaction policies have a respective average score within the specific tolerance of an optimal interaction policy score with respect to each of the plurality of user models; selecting, for the wireless communication device, up to the predetermined number of the learned ones of the plurality of interaction policies that are within the specific tolerance of the optimal interaction policy for the plurality of user models; and simulating, at least a predetermined number of times, user actions for each of the plurality of user models and each of the plurality of interaction policies, wherein the calculating of the score of each of the plurality of interaction policies with respect to each of the plurality of user models is based on the simulating.