A　heterogeneous　or　hybrid　5G　network　is　required　to　support　connected　vehicles　to　implement　the　full　range　of　cooperative　ITS　(intelligent　transport　system)　services　in　diverse　scenarios.　In　order　to　enhance　data　rate　or　reduce　latency　by　increasing　transmission　bandwidth,　5G　utilizes　frequency　bands　below　and　above　6　GHz.　The　challenge　is　that　multiple　band　coordination　in　5G　will　be　essential　to　mobile　network　operators.　Even　worse,　traditional　strategies　could　not　meet　the　demand.　Most　current　5G　research　is　focused　in　5G　network　optimization.　However,　frequency　coordination　in　5G,　as　one　of　the　most　important　requirements　from　operators,　is　left　untouched.　In　this　paper,　a　multi-agent　deep　Q-learning　network　(DQN)　is　developed　as　coordination　solution.　Transfer　learning　is　introduced　in　DQN　to　decrease　the　deployment　complexity　of　the　proposed　solution　on　5G　gNB　(next-generation　NodeB).　By　deploying　the　proposed　solution　in　the　system　level　simulation,　the　simulation　shows　an　average　10%　throughput　enhancement,　an　about　24%　accessed　user　number　increasing,　and　around　70%　training　time　saving,　compared　with　normal　Q-learning　solution,　and　it　enables　the　operators　to　optimally　utilize　all　the　valuable　frequency　resources　to　the　best　commercial　value.　©　2022　Hua-Min　Chen　et　al.