In the field of optical engineering, the laser position control system has important role in many applications, such as measurement, communication, fabrication. Traditional methods to solve laser position control system often face the problems of insufficient generalization, such as configuration or singular solution. In this study we proposed a novel model- free reinforcement learning approach based Proximal Policy Optimization (PPO) for laser position control system. To control the position of laser, we develop an efficient representation of environmental inputs and outputs. Position error of Position Sensing Detector (PSD), and three kinds of distance parameters are applied our environmental parameters. To overcome the challenges associated with training in real worlds, we developed training environment in simulation. The simulation to evaluate performance of our approach, we perform several times of experiments in both simulated and real world system.