Autostereoscopic three-dimensional (3D) display has attracted considerable attention in recent years. To achieve high-quality 3D display with the lenticular-lens array, the accurate 3D display parameters of the lenticular-lens array need to be measured, including lines per inch (LPI) and slanted angle. Traditional methods generally measured the 3D display parameters by manually observing white-black characteristic 3D images synthesized based on the LPI and slanted angle. However, these methods have problems with time-consuming, inaccurate estimations, or limited applications. To address these problems, a method based on deep reinforcement learning (DRL) is proposed, which can automatically measure the 3D display parameters of the lenticular-lens array. In our method, the white-black characteristic 3D image is initialized based on the coarse LPI and slanted angle. Then the 3D image is captured by a camera from a 3D display device and input into a convolutional neural network (CNN) based on DRL. Finally, the CNN is used to optimize the LPI and slanted angle, and the accurate 3D display parameters can be measured. Experimental results show that our method can efficiently measure accurate 3D display parameters with roughly initial parameter values. We hope our study will make a valuable contribution to the field of autostereoscopic 3D display.