Active　learning　aims　to　select　＇worthy＇　data　for　annotation　such　that　the　model　could　achieve　better　performance　using　as　less　labeled　data　as　possible.　Previous　research　works　mainly　use　heuristic　selection　methods　to　solve　this　problem.　Since　the　process　of　data　selection　and　model　training　is　separated,　these　methods　have　limitations　in　effectiveness.　This　paper　proposes　a　new　active　learning　framework,　which　uses　deep　reinforcement　learning　as　the　data　selection　strategy.　Instead　of　choosing　＇worthy＇　data　through　heuristic　algorithms,　we　use　the　deep　reinforcement　learning　algorithm　explicitly　learning　a　data　selection　policy.　The　deep　convolutional　neural　network　is　used　to　extract　images＇　features　which　serve　as　＇state＇　in　the　reinforcement　learning　algorithm.　And　we　use　deep　Q-learning　algorithm　to　train　a　Q-network.　According　to　the　output　of　the　Q-network　to　decide　taking　which　＇action＇,　i.e.　annotate　the　data　or　not.　The　framework　proposed　in　this　paper　can　be　trained　by　an　end-to-end　manner.　Comprehensive　experimental　evaluations　on　CIFAR-10,　CIFAR-100　and　SVHN　datasets　with　VGG-16　model　and　four　different　depth　ResNet　models　demonstrate　that　the　proposed　method　outperforms　those　state-of-art　active　learning　methods　for　the　task　of　image　classification.　©　2019　IEEE.