Temporal　knowledge　graphs　completion　(TKGC)　is　a　critical　task　that　aims　to　forecast　facts　that　will　occur　in　future　timestamps.　It　has　attracted　increasing　research　interest　in　recent　years.　Among　the　many　approaches,　reinforcement　learning-based　methods　have　gained　attention　due　to　their　efficient　performance　and　interpretability.　However,　these　methods　still　face　two　challenges　in　the　prediction　task.　First,　a　single　policy　network　lacks　the　capability　to　capture　the　dynamic　and　static　features　of　entities　and　relationships　separately.　Consequently,　it　fails　to　evaluate　candidate　actions　comprehensively　from　multiple　perspectives.　Secondly,　the　composition　of　the　action　space　is　incomplete,　often　guiding　the　agent　towards　distant　historical　events　and　missing　the　answers　in　recent　history.　To　address　these　challenges,　this　paper　proposes　a　Temporal　Knowledge　Graph　Completion　Based　on　a　Multi-Policy　Network(MPNet).　It　constructs　three　policies　from　the　aspects　of　static　entity-relation,　dynamic　relationships,　and　dynamic　entities,　respectively,　to　evaluate　candidate　actions　comprehensively.　In　addition,　this　paper　creates　a　more　diverse　action　space　that　guides　the　agent　in　investigating　answers　within　historical　subgraphs　more　effectively.　The　effectiveness　of　MPNet　is　validated　through　an　extrapolation　setting,　and　extensive　experiments　conducted　on　three　benchmark　datasets　demonstrate　the　superior　performance　of　MPNet　compared　to　existing　state-of-the-art　methods.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.