Mobile-edge　computing(MEC)　is　deemed　to　a　promising　paradigm.　By　deploying　high-performance　servers　on　the　mobile　access　network　side,　MEC　can　provide　auxiliary　computing　power　for　mobile　devices,　greatly　reducing　the　computing　pressure　of　mobile　devices　and　improving　the　quality　of　the　computing　experience.　In　this　paper,　we　consider　the　offloading　problem　of　tasks　in　single-user　MEC　system.　In　order　to　minimize　the　mean　energy　consumption　of　mobile　devices　and　the　mean　slowdown　of　tasks　in　the　queue,　we　propose　a　deep　reinforcement　learning(DRL)　based　task　offloading　algorithm,　and　a　new　reward　function　is　designed,　which　can　guide　the　algorithm　to　optimize　the　trade-off　between　mean　energy　consumption　and　mean　slowdown.　The　simulation　results　show　that　the　deep　reinforcement　learning　based　algorithm　outperforms　the　baseline　algorithms.　©　2019　Association　for　Computing　Machinery.