Deep reinforcement learning in continuous action space for autonomous robotic surgery.

Purpose: Reinforcement learning methods have shown promising results for the automation of sub-tasks in robotic surgery systems. With the development of these methods, surgical robots have been able to achieve good performances, so that they can be used in complex and high-risk environments such as surgical pattern cutting to reduce stress and pressure on the surgeon and increase surgical accuracy. This study has aimed at providing a deep reinforcement learning-based approach to control the gripper arm when cutting soft tissue in a continuous action space.