Novel segmentation algorithms can easily identify objects that are occludedor partially occluded, however in highly cluttered scenes the degree of occlusion is so high that some objects may not be visible to a static camera. In these scenarios, humans use action to change the configuration of the environment, elicit more information through perception, process the information before taking the next action. Reinforcement learning models this behavior, however unlike humans, the phase where perception data is understood is not included, as images are directly used as observations. The aim of this thesis is to establish a novel method that indirectly uses perception data for reinforcement learning to address the task of decluttering a scene using a static camera.