Recent　advances　in　Internet　of　Things　(IoT)　provide　plenty　of　opportunities　for　various　areas.　Nevertheless,　the　machine-to-machine　(M2M)　communications-based　IoT　develops　rapidly　but　suffers　from　extra　energy　consumption,　large　data　transmission　latency　as　well　as　overmuch　network　cost,　because　various　of　machine-type　communication　devices　(MTCDs)　are　deployed　in　the　network.　To　meet　the　requirements　of　energy　efficient　M2M　communications,　in　this　paper,　we　introduce　a　promising　technology　named　as　mobile　edge　computing　(MEC),　and　propose　a　performance　optimization　framework　with　MEC　for　M2M　communications　network　based　on　deep　reinforcement　learning　(DRL).　According　to　dynamic　decision　process　by　DRL,　the　appropriate　access　networks　and　the　computing　servers　can　be　determined　and　selected　with　the　minimum　system　cost,　which　includes　lower　network　cost,　time　cost　and　energy　consumption　for　data　transmission　and　computing　tasks　execution.　Extensive　simulation　results　with　different　system　parameters　show　that　our　proposed　framework　can　effectively　improve　the　system　performance　for　M2M　communications　compared　to　the　existing　schemes.　©　2019　IEEE.