论文标题
Defoggan：用生成的对抗网预测战争星空雾中的隐藏信息
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
论文作者
论文摘要
我们提出了Defoggan，这是一种生成的方法，用于推断隐藏在实时战略（RTS）游戏的战争中的状态信息的问题。鉴于部分观察到的状态，Defoggan生成了游戏的融化图像作为预测信息。这样的信息可以导致为游戏创建战略代理。 DeFoggan是一种有条件的GAN变体，具有锥体重建损失，可在多个特征分辨率量表上进行优化。我们使用大型专业星际争霸重播的大型数据集在经验上验证了Defoggan。我们的结果表明，Defoggan可以像专业参与者一样准确地预测敌人的建筑物和战斗单位，并在最先进的defogger中取得出色的表现。
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.
