We　explore　a　principle　method　to　address　the　weakly　supervised　detection　problem.　Many　deep　learning　methods　solve　weakly　supervised　detection　by　mining　various　object　proposal　or　pooling　strategies,　which　may　cause　redundancy　and　generate　a　coarse　location.　To　overcome　this　limitation,　we　propose　a　novel　human-like　active　searching　strategy　that　recurrently　ignores　the　background　and　discovers　class-specific　objects　by　erasing　undesired　pixels　from　the　image.　The　proposed　detector　acts　as　an　agent,　providing　guidance　to　erase　unremarkable　regions　and　eventually　concentrating　the　attention　on　the　foreground.　The　proposed　agents,　which　are　composed　of　a　deep　Q-network　and　are　trained　by　the　Q-learning　algorithm,　analyze　the　contents　of　the　image　features　to　infer　the　localization　action　according　to　the　learned　policy.　To　the　best　of　our　knowledge,　this　is　the　first　attempt　to　apply　reinforcement　learning　to　address　weakly　supervised　localization　with　only　image-level　labels.　Consequently,　the　proposed　method　is　validated　on　the　PASCAL　VOC　2007　and　PASCAL　VOC　2012　datasets.　The　experimental　results　show　that　the　proposed　method　is　capable　of　locating　a　single　object　within　5　steps　and　has　great　significance　to　the　research　on　weakly　supervised　localization　with　a　human-like　mechanism.