Robotic Arm Control Method Based on Reinforcement Learning Optimization

The actual working environment of the robotic arm is dynamic, and the operation model is complex. However, traditional control methods overly rely on precise models, require complex manual parameter tuning, and have problems such as low accuracy and weak adaptability. Accordingly, this study develops a robotic arm control method grounded in the Deep Deterministic Policy Gradient (DDPG) framework and establishes the simulation environment of the PyBullet robotic arm. The simulation environment consists of two parts: the robotic manipulator and the target item. State variables are set according to the target, and the design integrates a combined reward function along with an experience replay strategy. The DDPG is trained in the model to realize the regulation of the robotic arm's movement through the deep reinforcement learning algorithm, enabling the terminal actuator to move quickly and accurately to the target item. The results of the experiments indicate that the deep reinforcement learning algorithm is capable of achieving convergence rapidly, verifying that precise control of the movement of the robotic arm can be attained through the application of the DDPG algorithm.