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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

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #128

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.

Author Information

Gabriel Synnaeve (Facebook)
Zeming Lin (Facebook AI Research)
Jonas Gehring (Facebook AI Research)
Dan Gant (Facebook AI Research)
Vegard Mella (Facebook AI Research)
Vasil Khalidov (Facebook AI Research)
Nicolas Carion (Facebook AI Research Paris)
Nicolas Usunier (Facebook AI Research)

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