Casual　stereoscopic　photography　allows　ordinary　users　to　create　a　stereoscopic　photo　using　two　photos　taken　casually　by　a　monocular　camera.　The　visual　comfort　of　a　casual　stereoscopic　photo　can　greatly　affect　its　visual　experience.　In　this　paper,　we　present　a　novel　visual　comfort　enhancement　method　for　casual　stereoscopic　photography　via　reinforcement　learning　based　on　continuous　transformation　superposition.　We　consider　the　transformation,　in　a　continuous　transformation　space,　to　transform　each　view　as　superpositions　of　several　basic　continuous　transformations,　enabling　more　subtle　and　flexible　image　transformation　operations　to　approach　better　solutions.　To　achieve　the　continuous　transformation　superposition,　we　prepare　a　collection　of　continuous　transformation　models　for　translation,　rotation,　and　perspective　transformations.　Then　we　train　a　policy　model　to　determine　an　optimal　transformation　chain　to　recurrently　handle　both　the　geometric　constraints　and　disparity　adjustment,　and　thereby　enhance　the　visual　comfort　of　casual　stereoscopic　images.　We　further　propose　an　attention-based　stereo　feature　fusion　module　that　enhances　and　integrates　the　binocular　information　between　the　left　and　right　views.　Experimental　results　on　three　datasets　demonstrate　that　our　proposed　method　achieves　superior　performance　to　state-of-the-art　methods.