With　the　development　of　computer　technology,　computational-intensive　and　delay-sensitive　applications　are　emerging　endlessly,　and　they　are　limited　by　the　computing　power　and　battery　life　of　Smart　Mobile　Devices　(SMDs).　Mobile　edge　computing　(MEC)　is　a　computation　model　with　great　potential　to　meet　application　requirements　and　alleviate　burdens　on　SMDs　through　computation　offloading.　However,　device　mobility　and　server　status　variability　in　the　multi-server　and　multi-task　scenario　bring　challenges　to　the　computation　offloading.　To　cope　with　these　challenges,　we　first　propose　a　parallel　task　offloading　model　and　a　small　area-based　edge　offloading　scheme　in　MEC.　Then,　we　formulate　the　optimization　problem　to　minimize　the　completion　time　of　all　tasks,　and　transform　the　problem　into　a　deep　reinforcement　learning-based　offloading　scheme　by　Markov　decision　approach.　Furthermore,　we　present　a　deep　deterministic　policy　gradient　(DDPG)　approach　for　obtaining　the　offloading　strategy.　Experimental　results　demonstrate　that　the　DDPG-　based　offloading　approach　improves　long-term　performance　by　at　least　19%　in　ultra-low　latency,　efficient　usage　of　servers,　and　frequent　mobility　of　SMDs　over　traditional　strategies.