With the industry 4.0 embraced a number of contemporary automation, data exchange and manufacturing technologies, the autonomous driving system is widespread. In order to enable the autonomous driving, path following strategies are essential to maintain the normal work of the vehicles. The articulated frame steering vehicles (ASV) are flexible, efficient and widely implemented in agriculture, mining, construction and forestry sectors due to their high maneuverability. The articulated vehicle usually composes of two units, a tractor and a trailer, which are connected by an articulation joint. However, as the ASV dynamics are significantly different from the conventional vehicles with front wheel steering, the path following controller derived for conventional vehicles is considered not to be applicable for the ASVs. Thus the path following control is challenging the robustness. A path following strategy is proposed for the ASVs on the basis of reinforcement learning adaptive PID algorithm. The kinematic model of the ASV is derived by neglecting the vehicle dynamics. Three measurable errors are defined to indicate the deviation of real path from reference path, i.e., lateral displacement error, orientation error and curvature error. These errors are served as the inputs in order to synthesize the path following controller and the desired steering angle is served as the output of path following controller. Based on the PID algorithm, the reinforcement learning method is selected for optimizing the parameters of PID online to reduce the overshoot and chattering. Furthermore, the prototype test is conducted to evaluate the performance of the proposed control law. The result shows that compared with the traditional PID, reinforcement learning adaptive PID controller can restrain the overshoot and chattering efficiently and follow the reference path accurately.