  Deep reinforcement learning augments the reinforcement learning framework and
utilizes the powerful representation of deep neural networks. Recent works have
demonstrated the remarkable successes of deep reinforcement learning in various
domains including finance, medicine, healthcare, video games, robotics, and
computer vision. In this work, we provide a detailed review of recent and
state-of-the-art research advances of deep reinforcement learning in computer
vision. We start with comprehending the theories of deep learning,
reinforcement learning, and deep reinforcement learning. We then propose a
categorization of deep reinforcement learning methodologies and discuss their
advantages and limitations. In particular, we divide deep reinforcement
learning into seven main categories according to their applications in computer
vision, i.e. (i)landmark localization (ii) object detection; (iii) object
tracking; (iv) registration on both 2D image and 3D image volumetric data (v)
image segmentation; (vi) videos analysis; and (vii) other applications. Each of
these categories is further analyzed with reinforcement learning techniques,
network design, and performance. Moreover, we provide a comprehensive analysis
of the existing publicly available datasets and examine source code
availability. Finally, we present some open issues and discuss future research
directions on deep reinforcement learning in computer vision
