This　paper　proposes　an　object　detection　strategy　with　a　deep　reinforcement　learning　method　Double　DQN　in　which,　given　an　image　window,　a　deep　reinforcement　learning　agent　is　trained　to　determine　which　predefined　region　candidates　to　focus　the　attention　on.　In　the　Double　DQN　framework,　the　first　DQN　is　used　to　select　an　action　to　search　the　target　region　and　the　second　is　to　evaluate　the　selected　action.　In　order　to　verify　the　efficiency　of　our　method,　we　compare　the　performance　of　Double　DQN　with　the　traditional　DQN.　Experiments　indicate　Double　DQN　has　good　results　with　higher　precision　and　recall.　The　number　of　actions　performed　by　the　Double　DQN　agent　are　analyzed　and　the　results　show　that　the　object　can　be　found　within　very　few　steps.　We　also　conducted　an　experiment　on　person　detection,　the　results　show　that　the　algorithm　has　strong　adaptive　ability.　©　2017　IEEE.