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arXiv · 1812.00054

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

Gabriel Synnaeve·Zeming Lin·Jonas Gehring·Dan Gant·Vegard Mella·Vasil Khalidov·Nicolas Carion·Nicolas Usunier
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Abstract

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.

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Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier. 2018-11-30. Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger. https://arxiv.org/abs/1812.00054

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