Its highly dependent on implementation.

https://www.pugetsystems.com/labs/articles/stable-diffusion-performance-professional-gpus/

The experience on Linux is good (use docker otherwise python is dependency hell) but the basic torch based implementations (automatic, comfy) have bad performance. I have not managed to get shark to run on linux, the project is very windows focused and has no documentation for setup besides “run the installer”.

Basically all of the vram trickery in torch is dependent on xformers, which is low-level cuda code and therefore does not work on amd. And has a running project to port it, but it’s currently to incomplete to work.

That’s an interesting approach. The Traditional way would be to go by game score like the AI Mario Projects. But I can see the value in prioritizing Bullet Avoidance over pure score.

Does your training Environment Model that shooting at enemies (eventually) makes them stop spitting out bullets? I also would assume that total survival time is a part of the score, otherwise the Boss would just be a loosing game score wise.