In the classical examples of deep q-learning, I often see neural networks in which the input represents the state of the agent, while the output is a tuple with all the values of $Q(s, a)$ predicted for all the possible $N$ actions.

Would it be cheaper to have $N$ neural networks with a single real-valued output, one for each of the $N$ actions?

With cheaper I mean cheaper in terms of the time complexity of a single training step of the network.