Is it possible to use NEAT networks for solving video games?
Implementing the NEAT algorithm for playing video games is quite realistic, especially considering that it is not novel, so there are extensive resources and tutorials to help you.  In fact, there are publications that will walk you through exactly what you want to do, such as this paper on using the NEAT algorithm for playing Pong.

Of course designing a self-driving car would be significantly more advanced than playing the 2D games you mentioned, but it appears that much of the work for developing AI in GTA V exists.

The person you mentioned, Sentdex, has a github page for developing GTA V code.  However, note that:


  We read frames directly from the desktop, rather than working with the game's code itself. This means it works with more games than just GTA V, and it will basically learn (well, attempt to learn...) whatever you put in front of it based on the frames as input and key presses as output.


This is a very primitive approach.

I am by no means an expert on self-driving cars, but after some Googling, deep drive appears to be the premier simulated self-driving car development platform.  If you are going to develop the GTA network, I suggest checking out it out.


As for comparing to an implementation of AlexNet:

Again, I am not an expert on self-driving cars, but I would expect an ensemble of networks to be used for this type of task, so I imagine an object detection network or semantic segmentation network to be used in conjunction with a network designed with the NEAT algorithm.

For instance, you can use a fully convolutional AlexNet model to detect objects such as pedestrians/cars/sidewalks/etc and feed that data into another network that also takes other inputs such as your current speed, wheel angle, road conditions etc that combines all this information and translates it into actions such as applying the brakes, turning etc.

AlexNet is a Convolutional Neural Network (CNN), which is primarily used in the field of vision processing.  The NEAT algorithm typically generates Multilayer Perceptrons (MLP), which are not used individually for machine vision challenges; they are different tools for different tasks.  It would be like asking, "which is better: a hammer or a screwdriver".  The answer depends on the task.



TL;DR:

Yes, using NEAT for video game AI development is a good introductory project for some of the more advanced topics in machine learning and deep learning.