In　order　to　improve　the　task　offloading　efficiency　in　multi-access　edge　computing　(MEC),　a　joint　optimization　model　for　task　offloading　and　heterogeneous　resource　scheduling　was　proposed,　considering　the　heterogeneous　communication　resources　and　computing　resources,　jointly　minimizing　the　energy　consumption　of　user　equipment,　task　execution　delay,　and　the　payment.　A　deep　reinforcement　learning　method　is　adopted　in　the　model　to　obtain　the　optimal　offloading　algorithm.　Simulations　show　that　the　proposed　algorithm　improves　the　comprehensive　indexes　of　equipment　energy　consumption,　delay,　and　payment　by　27.6%,　compared　to　the　Banker＇s　algorithm.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Posts　and　Telecommunications.　All　right　reserved.