Could Active Perception Aid Navigation of Partially Observable Grid Worlds?

Abstract

Due to the unavoidable fact that a robot's sensors will be limited in some manner, it is entirely possible that it can find itself unable to distinguish between di#ering states of the world (the world is in e#ect partially observable). If reinforcement learning is used to train the robot, then this confounding of states can have a serious e#ect on its ability to learn optimal and stable policies. Good results have been achieved by enhancing reinforcement learning algorithms through the addition of memory or the use of internal models. In our work we take a di#erent approach and consider whether active perception could be used instead