There is a big overlap between Optimal Control and Reinforcement Learning, in case you didn't know.

Also Steve Brunton does a lot on the interface between control theory and ML on his channel: https://www.youtube.com/channel/UCm5mt-A4w61lknZ9lCsZtBw/pla...

Another thing to keep in mind is that having AI/ML surrogates which can evaluate expensive functions faster can also be integrated as an information source in model predictive control algorithms.

there's a lot of work in the broad area. most of it doesn't engage with the classical control theory literature (arguably it should).

some keywords to search for recent hot research would be "world model", "decision transformer", "active inference", "control as inference", "model-based RL".

Great job! I'm working in a similar blog post and it was fun seeing how you approached it. I was surprised the wasm implementation is fast enough, I was even considering writing webGpu compute shaders for my solver

Beautiful stuff, great post!