 Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy . The proposed unsupervised visual attention and invariance method (VAI) contains three key components: 1) an unsupervisedkeypoint detection model which captures semantically meaningful keypoints in observations; 2) an unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation;
<coherence>  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy . The proposed unsupervised visual attention and invariance method (VAI) contains three key components: 1) an unsupervisedkeypoint detection model which captures semantically meaningful keypoints in observations; 2) an unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation;
 Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy learner. Our method is completely unsupervised, without manual annotations or access to environment internals. Given videos of actions in a training environment, we learn how to extract foregrounds with unsupervised keypoint detection, followed by unsupervised visual attention module which automatically generates the distraction-invariant attention mask for each observation;