Before revision:  To improve the efficiency of deep reinforcement learning (DRL) based methods for robot manipulator trajectory planning in random working environment. Different from the traditional sparse reward function , we present three dense reward functions in this paper.
After revision:  To improve the efficiency of deep reinforcement learning (DRL) based methods for robot manipulator trajectory planning in random working environments , we present three dense reward functions in this paper.