Abstract:  A reinforcement learning method based on Deep Deterministic Policy Gradient(DDPG) is presented to more effectively tackle the problem of controlling a six-axis robotic arm in three-dimensional space in order to address the issues of low accuracy, stability, and executability of existing control algorithms in complicated situations. The simulation environment is established in the MuJoCo platform, the planned robotic arm is imported as the test object, and the DDPG algorithm, the Soft Actor-Critic Algorithms (SAC), and the Twin Delayed Deep Deterministic Policy Gradient (TD3) are utilized for repeated comparison tests in the simulation environment. The study demonstrates that the DDPG algorithm-based robotic arm control approach can successfully increase the accuracy and stability of robotic arm control, and that this algorithm is more stable than SAC and TD3.