Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. Relativized options are defined without an absolute frame of reference, and an option's policy is transformed suitably based on the circumstances under which the option is invoked. In earlier work we addressed the issue of learning the option policy online. In this article we develop an alogrithm for choosing, from among a set of candidate transformations, the right transformation for each member of the family of tasks.