Reinforcement Learning-Based Simulation of Seal Engraving Robot in the Context of Artificial Intelligence

The rapid development of robotics technology has made people's lives and work more convenient and efficient. The research and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in various fields of robot applications. In view of this, this study is based on deep reinforcement learning convolutional neural networks, combined with point cloud models, proximal strategy optimization algorithms, and flexible action evaluation algorithms. A seal cutting robot based on deep reinforcement learning has been proposed. The final results show that the descent speed of the seal cutting robot with the root mean square difference as the performance standard is about 1% faster than the flexible action evaluation algorithm. About 2% faster than the proximal strategy optimization algorithm. It is about 4% faster than the deep deterministic strategy gradient algorithm. This indicates that the research model has certain advantages in terms of actual accuracy after cutting. The fluctuation of this model is about 10% smaller than the evaluation of flexible actions and about 60% smaller than the gradient of deep deterministic strategies. Therefore, the