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We formulate the problem of defogging as state estimation and future stateprediction from previous, partial observations in the context of real-timestrategy games. We propose to employ encoder-decoder neural networks for thistask, and introduce proxy tasks and baselines for evaluation to assess theirability of capturing basic game rules and high-level dynamics. By combiningconvolutional neural networks and recurrent networks, we exploit spatial andsequential correlations and train well-performing models on a large dataset ofhuman games of StarCraft: Brood War. Finally, we demonstrate the relevance ofour models to downstream tasks by applying them for enemy unit prediction in astate-of-the-art, rule-based StarCraft bot. We observe improvements in winrates against several strong community bots.

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger