[anglinb]

This looks incredible, could see this approach revolutionizing so many industries.

[goodpoint]

Solar followers have been around for decades and do not need ML or complex models.

[rl_for_energy]

Thank you! RL will eat the world. I'm applying it to batteries optimization next

[SubiculumCode]

this is not my field, but you couldn't just have it orient to point of brightest intensity?

[rl_for_energy]

You certainly could, but that doesn't entirely account for shading / system degradation / site-specific diffuse light opportunities (consider a huge amount of light reflecting off the side of a mountain at some time of day). Those are both really difficult and time-intensive to model for, so there's a desire to have an AI that can simply learn those things specific to the system it's optimizing without humans having to do it. I see the larger impact of RL as scaling humanity's problem solving capability. If we have to use N human hours per installation to get to 97% optimality per installation but RL can use N/10000000 per installation to get to 95%, we could free up all those N human hours for things that RL still struggles with. Just my 2 cents though, it's a very fair question

[mirker]

Yeah but that requires knowing control theory rather than “learning” it.

Control theory is better if you know what you’re doing. ML is technical debt for sure.