Theoretically, a neural network could be trained to learn this. In practise, it would most likely be significantly slower, at least with the tools we have currently available. That’s why I don’t see a benefit in this approach at the moment.
It would be ridiculously complicated and require an unacceptable amount of computational power to train such a network with the technology we have available today. A company like Google might be able to get some results, but I doubt they could produce high quality renders with such an approach.
Instead of trying to replace path tracers like Cycles, there has been a lot of work to enhance them with machine learning. I believe that we are going to see a lot of renderers going towards this direction.
The denoiser paper from Disney and Pixar which I am replicating also contains a whole section which is dedicated to adaptive sampling. Adaptive sampling is really complicated because there is no precise known way how to deal with it. Instead it requires some sort of guessing and analyzing what might be best in general. Or in other words, some kind of “computer intuition” would help a lot. And that’s exactly what neural networks are really good for.
There is also interesting work to use neural networks to help with importance sample. Though this is not yet as advanced and improvements are likely still needed before it becomes practically relevant.