%0 Journal Article %A MA Enqi %A MAO Xiaobin %A WANG Gang %A WANG Jianping %T Motion control method of two-link manipulator based on deep reinforcement learning %D 2021 %R 10.11772/j.issn.1001-9081.2020091410 %J Journal of Computer Applications %P 1799-1804 %V 41 %N 6 %X Aiming at the motion control problem of two-link manipulator, a new control method based on deep reinforcement learning was proposed. Firstly, the simulation environment of manipulator was built, which includes the two-link manipulator, target and obstacle. Then, according to the target setting, state variables as well as reward and punishment mechanism of the environment model, three kinds of deep reinforcement learning models were established for training. Finally, the motion control of the two-link manipulator was realized. After comparing and analyzing the three proposed models, Deep Deterministic Policy Gradient (DDPG) algorithm was selected for further research to improve its applicability, so as to shorten the debugging time of the manipulator model, and avoided the obstacle to reach the target smoothly. Experimental results show that, the proposed deep reinforcement learning method can effectively control the motion of two-link manipulator, the improved DDPG algorithm control model has the convergence speed increased by two times and the stability after convergence enhances. Compared with the traditional control method, the proposed deep reinforcement learning control method has higher efficiency and stronger applicability. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020091410