

This is basically lucid dreaming.
If you make notes of what you remember of your dreams when you first wake up then after a few days you’ll become conscious while dreaming. Then you can basically decide what to dream.
It’s kind of fun flying around but I stopped doing it as I didn’t find it particularly restful.







Reinforcement learning involves a reward signal (e.g. a score in a game) which I don’t think is present here.
Diffusion models, as you’ve also mentioned, seem a better metaphor. These try to generate a structured image (e.g. matching a prompt) from noise. Perhaps your visual cortex is just trying to make sense of random sensory input while your eyes are closed.
It’s also interesting to think about dreaming in terms of the more general set of representation learning techniques. As I understand it you’re trying to process the day’s experiences and reflect on past memories - sifting through and deciding what to retain and what to forget - essentially mental filing.
You may be interested in Deep dream. This is a program that runs an e.g. convolutional neural network in reverse. Instead of adjusting it’s belief about whether an image should be classified as a dog or not it adjusts a given input image so that it looks more and more like a dog. The results are pretty psychedelic!