I have a project idea and would like some feedback on feasibility.

I want to create a ML model that I would use in a subsequent model training loop.

This first model would take a image of x by x dimensions as input and then output instructions to a custom Image Creation tool for steps of re-creating the image.

The instructions would be semi-human readable but mostly just for the program to interpret and would look like the following and be arguments for the custom image creation tool to take in.

> >412, 123 #FF00FF ----- This would turn this one pixel Fuschia > >130, 350 ; 150, 400 #000000 ----- This would turn this rectangle of pixels on the canvas to black.

And many more complex tools available to take in as arguments.

The reward function would have two stages. The first stage is how close is your image to the original which would be easy to compute. And the second stage reward function would reward instruction minimization. I.E. 5000 steps to recreate the image would be rewarded higher than 10000 steps.

It would also be easy to set the upper bound of recreating the image to the total pixel count for that image so that it can be killed if it reaches the limit without creating the 1:1 image it was given as input.

The program would also allow as input argument the ability to create custom functions. Which we would also the model the ability to do. One thing that would incentivize the model to create and use its custom functions is that the reward would be tweaked so that if the model uses a predefined function it creates it counts as less instructions than if it were to individually call those instructions.

This first model is all about training it to recreate images 1:1 in the least amount of discrete instructions as possible for any arbitrary image.

This model/program would then be used in a second models training loop which I would like to keep secret for now.