Knowledge　graphs　(KGs)　model　entities　or　concepts　and　their　relations　in　a　structural　manner.　The　incompleteness　has　turned　out　to　be　the　main　challenge　that　hinders　the　application　of　KGs.　Recently,　reinforcement　learning　(RL)　has　been　recognized　as　an　effective　method　to　deal　with　such　a　challenge,　which　models　research　tasks　into　a　sequence　decision　problem　without　labels.　Although　an　increasing　number　of　studies　investigate　and　analyze　KGs　using　RL,　there　lacks　a　systematic　literature　review　that　comprehensively　and　quantitatively　analyzes　the　landscape　of　RL-based　KG　research　(RL-KG　for　short).　As　a　result,　researchers　may　have　encountered　difficulties　in　appropriately　adopting　RL　techniques　in　KG　research,　even　reinventing　the　wheels.　In　this　paper,　we　follow　the　Systematic　Literature　Review　(SLR)　methodology　to　survey,　screen,　and　investigate　papers　of　RL-KG.　Specifically,　we　identify　109　highly　related　papers　from　1542,　and　systematically　investigate　them　with　regard　to　the　following　five　research　questions:　(1)　to　what　extent　RL-KG　have　been　investigated;　(2)　what　application　domains　have　been　covered;　(3)　what　RL　techniques　have　been　mainly　considered;　(4)　whether　there　is　a　connection　between　the　influence　and　reproducibility　of　these　papers;　(5)　what　specialized　datasets,　evaluation　metrics,　and　publication　venues　have　been　applied.　Through　an　in-depth　analysis　of　the　review　results,　we　systematically　and　comprehensively　identify　some　significant　phenomena　and　analyze　the　reasons　and　difficulties　of　these　phenomena.　Based　on　such　analysis,　we　tentatively　propose　promising　future　research　topics　to　promote　the　RL-KG.