  We formulate the problem of defogging as state estimation and future state
prediction from previous, partial observations in the context of real-time
strategy games. We propose to employ encoder-decoder neural networks for this
task, and introduce proxy tasks and baselines for evaluation to assess their
ability of capturing basic game rules and high-level dynamics. By combining
convolutional neural networks and recurrent networks, we exploit spatial and
sequential correlations and train well-performing models on a large dataset of
human games of StarCraft: Brood War. Finally, we demonstrate the relevance of
our models to downstream tasks by applying them for enemy unit prediction in a
state-of-the-art, rule-based StarCraft bot. We observe improvements in win
rates against several strong community bots.
