An Efficient and Accurate DDPG-Based Recurrent Attention Model for Object Localization

Using image processing algorithm to localize objects, which lack specific patterns and local features, has always been the research focus in industrial production. Compared with the traditional image processing algorithm, RAM (Recurrent Attention Model) in deep learning not only shows advantages in positioning accuracy and stability, but also has good adaptability in situations such as occlusion. However, RAM contains policy gradient (PG) algorithm, which is unstable in training process and has low convergence efficiency. To overcome this shortcoming, in order to improve the learning efficiency and stability of RAM, this paper proposes DDPG-based RAM. In addition, current random sampling algorithm in DDPG (Deep Deterministic Policy Gradient) does not make full use of the information contained in samples. Some samples are repeatedly learned, which slows down the convergence rate of the neural network model, and even causes the model to converge to the local optimal solution. To solve the above problems, a prioritized experience replay algorithm based on Gaussian sampling method is proposed. By constructing the localization and grasping simulation environment in V-rep, it is shown that compared with the traditional image algorithm, the proposed model algorithm in this paper has a greater improvement in localization accuracy, stability and model convergence speed.