Mobile-edge　computing(MEC)　is　considered　to　be　a　new　network　architecture　concept　that　provides　cloud-computing　capabilities　and　IT　service　environment　for　applications　and　services　at　the　edge　of　the　network,　and　it　has　the　characteristics　of　low　latency,　high　bandwidth　and　real-time　access　to　wireless　network　information.　In　this　paper,　we　mainly　consider　task　scheduling　and　offloading　problem　in　mobile　devices,　in　which　the　computation　data　of　tasks　that　are　offloaded　to　MEC　server　have　been　determined.　In　order　to　minimize　the　average　slowdown　and　average　timeout　period　of　tasks　in　buffer　queue,　we　propose　a　deep　reinforcement　learning　(DRL)　based　algorithm,　which　transform　the　optimization　problem　into　a　learning　problem.　We　also　design　a　new　reward　function　to　guide　the　algorithm　to　learn　the　offloading　policy　directly　from　the　environment.　Simulation　results　show　that　the　proposed　algorithm　outperforms　traditional　heuristic　algorithms　after　a　period　of　training.