  We improve reliable, long-horizon, goal-directed navigation in
partially-mapped environments by using non-locally available information to
predict the goodness of temporally-extended actions that enter unseen space.
Making predictions about where to navigate in general requires non-local
information: any observations the robot has seen so far may provide information
about the goodness of a particular direction of travel. Building on recent work
in learning-augmented model-based planning under uncertainty, we present an
approach that can both rely on non-local information to make predictions (via a
graph neural network) and is reliable by design: it will always reach its goal,
even when learning does not provide accurate predictions. We conduct
experiments in three simulated environments in which non-local information is
needed to perform well. In our large scale university building environment,
generated from real-world floorplans to the scale, we demonstrate a 9.3\%
reduction in cost-to-go compared to a non-learned baseline and a 14.9\%
reduction compared to a learning-informed planner that can only use local
information to inform its predictions.
