An experience-based policy gradient method for smooth manipulation

被引:0
|
作者
Wang, Yongchao [1 ]
Lan, Xuguang [1 ]
Feng, Chuzhen [1 ]
Wan, Lipeng [1 ]
Li, Jin [1 ]
Liu, Yuwang [1 ]
Li, Decai [1 ]
机构
[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Shaanxi, Peoples R China
关键词
Policy Gradient; Robot Manipulation; Deep Reinforcement Learning;
D O I
10.1109/cyber46603.2019.9066580
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Policy gradient methods have achieved remarkable success in continuous controlling tasks. However, in robotic control, original policy gradient algorithms depend on the first succeed experience which is usually a suboptimal solution. To improve the performance, we propose an experience-based policy gradient method(EBDDPG) which guides the robot to move in a smooth way. Besides, extra OU-noise is added to the action space to improve exploration. We tested our algorithm on Gazebo simulation environment with Baxter robot. The experimental results show our method guides the robot to manipulate more smoothly and improves success rate of grasping tasks.
引用
收藏
页码:93 / 97
页数:5
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