Creating　stereoscopic　3D　media　content　has　wide　applications　in　virtual　reality.　In　this　paper,　we　are　interested　in　a　challenging　application,　casual　stereoscopic　photography,　that　allows　ordinary　users　to　create　a　stereoscopic　photo　using　two　images　captured　by　a　hand-held　monocular　camera.　To　handle　the　geometric　constraints　and　disparity　adjustment　for　casually　captured　left　and　right　images,　we　present　a　coarse-to-fine　framework.　In　the　coarse　stage,　we　propose　a　unified　reinforcement　learning-based　method,　in　which　the　produced　stereo　image　is　iteratively　adjusted　and　evaluated　in　the　term　of　visual　comfort.　In　addition,　to　further　enhance　the　visual　comfort　of　the　stereoscopic　image　produced　in　the　coarse　stage,　we　introduce　another　independent　recurrent　network　to　fine-tune　its　disparity　range.　Lastly,　we　perform　comprehensive　experiments　to　evaluate　our　method　and　demonstrate　the　applicability　of　our　model　for　real　images.