Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with
  Generative Adversarial Nets   We propose DefogGAN, a generative approach to the problem of inferring state
information hidden in the fog of war for real-time strategy (RTS) games. Given
a partially observed state, DefogGAN generates defogged images of a game as
predictive information. Such information can lead to create a strategic agent
for the game. DefogGAN is a conditional GAN variant featuring pyramidal
reconstruction loss to optimize on multiple feature resolution scales.We have
validated DefogGAN empirically using a large dataset of professional StarCraft
replays. Our results indicate that DefogGAN can predict the enemy buildings and
combat units as accurately as professional players do and achieves a superior
performance among state-of-the-art defoggers.

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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio