关键词: 深度强化学习, 机械臂, 轨迹规划, 方位奖励函数

Abstract: For the trajectory planning of robot manipulator in unknown environments, current deep reinforcement learning based?methods often suffer from the low learning efficiency and low robustness of planning strategy. To overcome the problems above, a novel azimuth reward function based trajectory planning method called A-DPPO is proposed. A novel azimuth reward function based on relative orientation and relative position is designed to reduce the invalid explorations and improve the learning efficiency. Moreover, it is the first time that Distributed Proximal Policy Optimization（DPPO） is applied to the trajectory planning for robot manipulator to improve the robustness of planning strategy. Experimental results show that the proposed A-DPPO method can increase the learning efficiency, compared to the state-of-the-art methods, and improve the robustness of planning strategy greatly.

Key words: deep reinforcement learning, robot manipulator, trajectory planning, azimuth reward function