With　the　arrival　of　the　5th　generation　mobile　networks　(5　G)　era,　the　data　needed　by　mobile　devices　(MDs)　is　explosively　growing.　High-consumption,　low-latency　applications　are　huge　challenges　for　resource-constrained　Internet　of　things　(IoT)　devices.　Mobile　edge　computing　overcomes　the　limitations　of　computing　resources　on　MDs　by　offloading　tasks　generated　by　MDs　and　assigning　them　to　nearby　MEC　servers.　Therefore,　mobile　edge　computing　(MEC)　becomes　important.　This　paper　presents　a　task　offloading　strategy　for　the　multi-device　multi-server　system.　To　meet　the　task　requirements　of　different　MDs,　we　formulate　an　overhead　minimization　problem　to　optimize　the　delay　and　energy　consumption　of　the　system.　We　propose　the　Double　Deep　Q　Network　(Double-DQN)　algorithm　to　perform　location　selection　strategies　for　tasks　generated　on　the　mobile　devices　and　allocate　respective　computing　resources.　Simulation　results　show　that　the　algorithm　can　allocate　resources　reasonably　and　reduce　the　overhead　of　the　entire　system.