I don't follow. Linear programming (with the simplex method) finds the optimal solution set for any linear program eventually. How can reinforcement learning be "better" apart from efficiency?
There are many problems that are hard to represent such that LP can compute them. That process alone is long, and hard to adjust even though clients need to make those adjustments.
These problems include multi-echelon inventory optimizations, and load-balancing across multiple assembly lines in factories when using hundreds of delivery vehicles, among others. These are properly multi-agent problems that RL solves well if given a valid simulation of the environment. LP often cannot solve it at all.
In cases where we went head to head with well known LP software vendors, we were able to move the Pareto frontier by double digit percentages.
We open sourced our software here after the acquihire. Depends on Gym among other repositories. Does something similar to Microsoft’s Bonsai. All are welcome to use.
The main challenge is to optimize these decisions. With optimality guarantees. Consistently. Day in, day out, with various uncertainties.
That is what math optimization has been doing for decades.
And no, RL is nowhere to be seen in production, except from some consulting projects that are abandoned.
No, this is mathematically impossible. You cannot out-optimize a mathematical global optimization solver. Whether that is LP, MILP, or NLP. Exactly because they provide a certificate of optimality.
It is like stating that you found a non-negative number that is smaller than 0. It’s not progressive. It’s flat out wrong.
"But there are lots of situations that LP can't optimize while RL can"
I will be satisfied if you just show us 1.
Stay in your lane, and state that you built a fast heuristic that works (finds good solutions but not optimal) in the problems you tried. Nobody can dispute this. But don't make claims you cannot support.
actually i guess they're shilling - not that there's that much of a differnce.