Autonomy holds substantial potential in bringing benefits to the robotic surgery industry. In this project, we focused on leveraging deep reinforcement learning methods to automate reaching and grasping operations during a robotic-assisted surgery with a developed handheld controller for manual control. The user study showed that the proposed framework can reduce the completion time (T) by 19.1% and the travel length (M) by 58.7%.