Optimal Path Search for Robot Manipulator using Deep Reinforcement Learning

Much research using deep reinforcement learning has been conducted to make robot manipulators perform certain tasks with little prior knowledge of the work environment. However, due to the high-dimension continuous action-state spaces that a robot manipulator must work in, it is difficult for deep reinforcement learning agents to find or even learn optimal policies. In this paper, we present a method for generating optimal paths for the end...