視覚的かくれんぼ：身体性エージェントによる追跡回避行動の学習
Visual Hide and Seek
獲物と捕食者のエージェントが一人称視点の部分観測下でかくれんぼを学習し、獲物は障害物を利用して自分の可視性を予測しながら逃げる方策を獲得する。
著者: Boyuan Chen, Shuran Song, Hod Lipson, Carl Vondrick
分類: cs.AI, cs.CV, cs.LG, cs.MA, cs.RO
原文アブストラクト
We train embodied agents to play Visual Hide and Seek where a prey must navigate in a simulated environment in order to avoid capture from a predator. We place a variety of obstacles in the environment for the prey to hide behind, and we only give the agents partial observations of their environment using an egocentric perspective. Although we train the model to play this game from scratch, experiments and visualizations suggest that the agent learns to predict its own visibility in the environment. Furthermore, we quantitatively analyze how agent weaknesses, such as slower speed, effect the learned policy. Our results suggest that, although agent weaknesses make the learning problem more challenging, they also cause more useful features to be learned. Our project website is available at: http://www.cs.columbia.edu/ ~bchen/visualhideseek/.
