Combining a DQN with a CNN

Humans play video games using their sight. They look at the screen, analyze the situation, and decide what the best action to be performed is. In video games, there can be a lot of things happening on the screen, so being able to see all these patterns can give a significant advantage in playing the game. Combining a DQN with a CNN can help a reinforcement learning agent to learn the right action to take given a particular situation.

Instead of just using fully connected layers, a DQN model can be extended with convolutional layers as inputs. The model will then be able to analyze the input image, find the relevant patterns, and feed them to the fully connected layers responsible for predicting the Q-values, as shown in the following:

Figure 10.6: Difference between a normal DQN and a DQN combined with convolutional layers

Adding convolutional layers helps the agent to better understand the environment. The DQN agent that we will...