  Efficient spatial exploration is a key aspect of search and rescue. In this
paper, we present a search algorithm that generates efficient trajectories that
optimize the rate at which probability mass is covered by a searcher. This
should allow an autonomous vehicle find one or more lost targets as rapidly as
possible. We do this by performing non-uniform sampling of the search region.
The path generated minimizes the expected time to locate the missing target by
visiting high probability regions using non-myopic path generation based on
reinforcement learning. We model the target probability distribution using a
classic mixture of Gaussians model with means and mixture coefficients tuned
according to the location and time of sightings of the lost target. Key
features of our search algorithm are the ability to employ a very general
non-deterministic action model and the ability to generate action plans for any
new probability distribution using the parameters learned on other similar
looking distributions. One of the key contributions of this paper is the use of
non-uniform state aggregation for policy search in the context of robotics.
