[tnecniv]

While I disagree with the RL assertion without a source, linear programs are convex, so local optima are global optima.

However, unless there is some aspect of the problem which is not known (e.g., you don’t exactly know the objective or constraints), so you model it as a distribution over LPs, I really don’t know how RL will help you. Gradient-based methods can give you improvements if your problem is very large scale and doesn’t have, e.g., sparse structure, but the above claim is bold.

[bshipp]

That's an excellent point. I think one complex agriculture issue I've encountered that LP would struggle to handle is something like inventory control of perishable goods, where there's variance in incoming quality, degradation during storage, and variance in shelf-life based on age of crop, growing conditions, etc. LP would work great for handling grain storage, where a crop generally maintains a known quality profile over time with limited shrinkage, but I could see a use for a machine learning algorithm that handles perishable goods stochastic variables in a more direct way.