Fusing　low　dynamic　range　(LDR)　for　high　dynamic　range　(HDR)　images　has　gained　a　lot　of　attention,　especially　to　achieve　real-world　application　significance　when　the　hardware　resources　are　limited　to　capture　images　with　different　exposure　times.　However,　existing　HDR　image　generation　by　picking　the　best　parts　from　each　LDR　image　often　yields　unsatisfactory　results　due　to　either　the　lack　of　input　images　or　well-exposed　contents.　To　overcome　this　limitation,　we　model　the　HDR　image　generation　process　in　two-exposure　fusion　as　a　deep　reinforcement　learning　problem　and　learn　an　online　compensating　representation　to　fuse　with　LDR　inputs　for　HDR　image　generation.　Moreover,　we　build　a　two-exposure　dataset　with　reference　HDR　images　from　a　public　multiexposure　dataset　that　has　not　yet　been　normalized　to　train　and　evaluate　the　proposed　model.　By　assessing　the　built　dataset,　we　show　that　our　reinforcement　HDR　image　generation　significantly　outperforms　other　competing　methods　under　different　challenging　scenarios,　even　with　limited　well-exposed　contents.　More　experimental　results　on　a　no-reference　multiexposure　image　dataset　demonstrate　the　generality　and　effectiveness　of　the　proposed　model.　To　the　best　of　our　knowledge,　this　is　the　first　work　to　use　a　reinforcement-learning-based　framework　for　an　online　compensating　representation　in　two-exposure　image　fusion.