 However, generalizing vision-based RL policy to unknown test environments still remains as a challengingproblem. 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 .
<clarity>  However, generalizing vision-based RL policy to unknown test environments still remains as a challengingproblem. 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 .
 However, generalizing vision-based RL policy to unknown test environments remains challenging. Existing methods 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 .