Figure 3.

An illustration of the architecture of our model. The input image is assigned to four convolution layers. The output of the convolution layers is split into two streams. The first stream (bottom) flattens the output and feeds it to an LSTM. The second one (top) flattens the output and feeds it to a fully connected layer. Then, we obtained an importance stream and value stream individually and multiplied them as the output. We stored the information in prioritized experience memory unit. As was shown in Figure 1, the network was trained using the DRQN loss function.