Thanks for the feedback! To address some of your points:
Yeah. Bio inspired is a graveyard of cargo cults with mediocre ideas, poor implementations, and zero results. ML people look down on the field for historically validated, if not universally correct, reasons. The wings don't need to flap, but they DO need to twist -- iykyk
I could write about the snake implementations specifically, I mostly put that aside to just focus on Atari 100k as it significantly more publishable/relevant.
Yes, the paper is a refinement of a specific algorithm that makes it more applicable to always-on, always-learning systems, really it's just a thing that I built for my own purposes, then realized that it was actually a novel contribution, and worth writing up, for the practice if nothing else. Absolutely the actual brain to play Atari games is *significantly* more complex than just this algorithm.
Why Snake, why Atari? My impression is that the real limiter we're hitting in the field is the inability to run continuous, adaptable intelligences on smaller, edge hardware. So, my internal roadmap is Atari 100k, then the harder version of Atari 100k (take off the training wheels that are typically part of the benchmark), then drone racing and harder video games, then different types of robotics and significantly harder video games. Eventually: practical brains that can be deployed into robot bodies like Optimus or Unitree, and produce actual value output in the real world. The video games are a ramp that leads to physical environments. I think. The original Deepmind Atari solutions didn't matter in the end because they weren't solving in a generalizable way, and they were using way too much compute. Afaict.