Before revision:  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 .
After revision:  Unlike previous works that focus on training a universal RL policy that is invariant to discrepancies between test and training environment, we focus on extracting visual foreground that is universal, feeding clean invariant vision to the RL policy .