  We address the task of long-horizon navigation in partially mapped
environments for which active gathering of information about faraway unseen
space is essential for good behavior. We present a novel planning strategy
that, at training time, affords tractable computation of the value of
information associated with revealing potentially informative regions of unseen
space, data used to train a graph neural network to predict the goodness of
temporally-extended exploratory actions. Our learning-augmented model-based
planning approach predicts the expected value of information of revealing
unseen space and is capable of using these predictions to actively seek
information and so improve long-horizon navigation. Across two simulated
office-like environments, our planner outperforms competitive learned and
non-learned baseline navigation strategies, achieving improvements of up to
63.76% and 36.68%, demonstrating its capacity to actively seek
performance-critical information.
