Abstract In this paper, we propose an automatic seed generation technique with deep reinforcement learning to solve the interactive segmentation problem. One of the main issues of the interactive segmentation problem is robust and consistent object extraction with less human effort. Most of the existing algorithms highly depend on the distribution of inputs, which differs from one user to another and hence need sequential user interactions to achieve adequate performance. In our system, when a user fifirst specififies a point on the desired object and a point in the background, a sequence of artifificial user input is automatically generated for precisely segmenting the desired object. The proposed system allows the user to reduce the number of input signifificantly. This problem is diffificult to cast as a supervised learning problem because it is not possible to defifine globally optimal user input at some stage of the interactive segmentation task. Hence, we formulate automatic seed generation problem as Markov Decision Process (MDP) and then optimize it by reinforcement learning with Deep QNetwork (DQN). We train our network on the MSRA10K dataset and show that the network achieves notable performance improvement from inaccurate initial segmentation on both seen and unseen datasets