There are various architectures you can use when setting up the Q-network. In the example you mentioned and most examples that have a Q-critic in Reinforcement Learning Toolbox the state and action path are separated. The reason is that you can architect these paths as necessary to extract useful features. For instance, in this example, one the state input is an image and the action is scalar torque. The image path needs to go through convolutional layers for example to extract features, but this is not necessary for a scalar input. This is why these paths are separated.
You can visualize neural networks in two ways:
and load criticNetwork from workspace to see an interactive representation of the critic.