Deep reinforcement learning will transform manufacturing as we know it
I have tested numerous RL algorithms on simulated production systems for my PhD thesis. It was a complete failure.<p>Most production planning problems are inherently high-dimensional and come with complex constraints. When you add noise to the problem, like random demand, all generic RL algorithms ran into trouble once you added complexity (like producing multiple products on the same line). So if you’re looking for good examples to break RL codes, throw them a nice stochastic economic lot sizing problem with sequence-dependent setup times.<p>By constrast what works well is policy search over parametric policies that consider domain knowledge. Sounds boring, but is quite hard to beat.