To improve the speed and accuracy of salient object detection in complex scenes, a two-stage salient object detection method based on deep reinforcement learning is proposed. It is composed of the salient region location network (SRLN) and the salient object segmentation network (SOSN), corresponding to the salient region location stage and the salient object segmentation stage respectively. In the salient region location stage, deep reinforcement learning (DRL) is used to train an agent to locate to the salient region gradually through sequence actions. To the best of our knowledge, it is the first to use DRL-based method for salient object detection. Then the fine salient object segmentation result can be obtained via a simple segmentation network. To simplify the network structure and reduce the number of parameters, SRLN and SOSN share feature extraction network. At the same time, a divide-and-conquer training strategy is proposed for the two-stage salient object detection framework. Experimental results on public datasets show that no matter for simple or complex scene images, the proposed algorithm can eliminate the disturbing information effectively and rapidly, achieving accurate and real-time performance. The extended experiments on pedestrian detection dataset show the generalization ability of our method in other application problems.