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Deep Reinforcement Learning for Robotic Arm Control: A Survey
LI Yanjiang,WANG Chensheng *,YANG Guang,JING Xueliang,LI Yangguang,QIAN Zhixuan
School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876
*Correspondence author
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Funding: Jiangsu Province Key Research Project on Industry Prospect and Common Key Technologies （No.BE2016032）, Wuxi Science and Technology Development Project on Industry Prospect and Common Technology （No.CGE02G1609）
Opened online:26 November 2019
Accepted by: none
Citation: LI Yanjiang,WANG Chensheng,YANG Guang.Deep Reinforcement Learning for Robotic Arm Control: A Survey[OL]. [26 November 2019] http://en.paper.edu.cn/en_releasepaper/content/4749929
 
 
Deep reinforcement learning (DRL) shows great performance in solving decision-making problems, in which an agent interacts with the environment and learns how to behavior. On the other side, complex robotics control missions offer DRL new challenging applications. This paper surveys the application of deep reinforcement learning in robotic arm control. It starts with the background of deep reinforcement learning and robotics then reviews different algorithms of DRL, introduces some typical deep reinforcement learning models for continuous control and then discusses the applications of DRL in robotic arms; finally, we will conclude the paper.
Keywords:deep reinforcement learning; continuous control; robotic arms; robot learning
 
 
 

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