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in the fundamentals of laparoscopic surgery standard medical training regimen the pattern cutting task requires students to demonstrate proficiency by controlling two actuators one with surgical scissors and the other with a grasping tool to accurately cut a circular pattern on a sheet of surgical gauze suspended at the corners accuracy can be improved by tensioning where the grasping tool grasps and pulls the tissue along a sequence of directions to induce tension in the material as cutting proceeds as deformation is difficult to sense we explore how deep reinforcement learning with trust region policy optimization can find tensioning policies using a finite element fabric simulator we compared the deep RL tensioning policies with fixed and analytic policies on a set of 17 open and closed curved patterns in simulation and for patterns and physical experiments with the DaVinci research kit our simulation results suggest that learning detention with deep reinforcement learning can sign