# Confusion about neural network architecture for the actor critic reinforcement learning algorithm

I am trying to understand the implementation of the actor critic reinforcement learning algorithm. According to this, there should be just one neural network with two heads for the action probabilities and the state values. It is also true in their tensorflow implementation here.

However, if I refer to the tensorflow implementations such as this and this, they use two nn and update them separately.

So, my question is what is the correct way to go about the neural network for the actor critic algorithm?