[ke88y]

> how easy is it to know that a reinforcement learning solution is actually a global optima and not just local?

If the problem is too complex for LP then you're probably not going to get a global optimal from RL either.

[bshipp]

Fair enough. I'm not invested in LP or RL, but my background is in agriculture and LP forms the basis of a lot of cropping/feeding decision trees. I'd be curious to explore new techniques, but it sounds like the discussion here is for a different set of linear programming problems than the ones I see most often, and that there's likely a limited upside toward implementing RL.

[ke88y]

If you have a working LP solution without any glaring compromises in the problem formulation, then I'm not sure why one would want to throw out a perfectly good working solution... algorithms are the means, not the ends :)

[bshipp]

I'm pretty sure the main reason for throwing out old tools that still function in favor of the new hotness is developer boredom.

[ke88y]

This is why my teams always provide explicit opportunities and spaces for professional development. People should have the opportunity to stretch and grow; if you don't provide those opportunities explicitly, then your most motivated employees will find them implicitly. And you can't afford to not keep your most motivated employees, so you'll end up paying with tech debt instead of Engineeer prof development time.