An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms

Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other. In addition, the experience replay mechanism of DDPG is also improved by combining priority sampling and uniform sampling to accelerate the DDPG's convergence. Finally, it is verified in the simulation environment that the improved DDPG algorithm can achieve accurate control of the robot arm motion. The experimental results show that the improved DDPG algorithm can converge in a shorter time, and the average success rate in the robotic arm end-reaching task is as high as 91.27%. Compared with the original DDPG algorithm, it has more robust environmental adaptability.

Automated summary

To address the issues of poor robustness and adaptability in traditional control methods, the deep deterministic policy gradient (DDPG) algorithm is improved by incorporating a hybrid function with multiple rewards. The experience replay mechanism of DDPG is also enhanced through a combination of priority sampling and uniform sampling, resulting in accelerated convergence. Experimental results in a simulation environment demonstrate that the improved DDPG algorithm achieves accurate control of robot arm motion, with a higher success rate of 91.27% compared to the original DDPG algorithm, thereby exhibiting improved environmental adaptability.