 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 .
<meaning-changed>  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 .
 Unlike previous works that focus on training a universal RL policy that is universal to changing visual domains, whereas we focus on developing an independent module to disperse interference factors irrelevant to the task, thereby providing "clean " observations for the RL policy .