Building a DRQN

A DQN can benefit greatly from RNN models facilitating the processing of sequential images. Such an architecture is known as Deep Recurrent Q Network (DRQN). Combining a GRU or LSTM model with a CNN model will allow the reinforcement learning agent to understand the movement of the ball. To do so, we just need to add an LSTM (or GRU) layer between the convolutional and fully connected layers, as shown in the following figure:

Figure 10.9: DRQN architecture

To feed the RNN model with a sequence of images, we need to stack several images together. For the Breakout game, after initializing the environment, we will need to take the first image and duplicate it several times in order to have the first initial sequence of images. Having done this, after each action, we can append the latest image to the sequence and remove the oldest one in order to maintain the exact same size of sequence (for instance, a sequence of a maximum of four images).

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