Most　reinforcement　learning　methods　describe　sequential　decision-making　as　a　Markov　decision　process　where　the　effect　of　action　is　only　decided　by　the　current　state.　But　this　is　reasonable　only　if　the　state　is　correctly　defined　and　the　state　information　is　sufficiently　observed.　Thus　the　learning　efficiency　of　reinforcement　learning　methods　based　on　Markov　decision　process　is　limited　when　the　state　information　is　insufficient.　Partially　observable　Markov　decision　process　and　history-based　decision　process　are　respectively　proposed　to　describe　sequential　decision-making　with　insufficient　state　information.　However,　these　two　processes　are　easy　to　ignore　the　important　information　from　the　current　observed　state.　Therefore,　the　learning　efficiency　of　reinforcement　learning　methods　based　on　these　two　processes　is　also　limited　when　the　state　information　is　insufficient.　In　this　paper,　we　propose　a　multi-view　reinforcement　learning　method　to　solve　this　problem.　The　motivation　is　that　the　interaction　information　between　the　agent　and　its　environment　should　be　considered　from　the　views　of　history,　present,　and　future　to　overcome　the　insufficiency　of　state　information.　Based　on　these　views,　we　construct　a　multi-view　decision　process　to　describe　sequential　decision-making　with　insufficient　state　information.　A　multi-view　reinforcement　learning　method　is　proposed　by　combining　the　multi-view　decision　process　and　the　actor-critic　framework.　In　the　proposed　method,　multi-view　clustering　is　performed　to　ensure　that　each　type　of　sample　can　be　sufficiently　exploited.　Experiments　illustrate　that　the　proposed　method　is　more　effective　than　the　compared　state-of-the-arts.　The　source　code　can　be　downloaded　from　https://github.com/jamieliuestc/MVRL.