Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models

by sebzuddas on 3/30/2026, 7:34:04 AM

https://dani2442.github.io/posts/continuous-rl/

Comments

by: Cloudly

Ever since the control bug bit me in my EE undergrad years I am happy to see how useful the knowledge remains. Of course the underlying math of optimization remains general but the direct applications of control theory made it much more appetizing for me to struggle through.

3/30/2026, 8:31:51 AM

by: lain98

I find myself completely outclassed by mathematicians in my own field. I tried to learn a little math on the side after my regular software engineer gig but I'm completely outclassed by phd's.<p>I am unsure of the next course of action or if software will survive another 5 years and how my career will look like in the future. Seems like I am engaged in the ice trade and they are about to invent the refrigerator.

3/30/2026, 11:00:06 AM

by: measurablefunc

It's not clear or obvious why continuous semantics should be applicable on a digital computer. This might seem like nitpicking but it's not, there is a fundamental issue that is always swept under the rug in these kinds of analysis which is about reconciling finitary arithmetic over bit strings & the analytical equations which only work w/ infinite precision over the real or complex numbers as they are usually defined (equivalence classes of cauchy sequences or dedekind cuts).<p>There are no dedekind cuts or cauchy sequences on digital computers so the fact that the analytical equations map to algorithms at all is very non-obvious.

3/30/2026, 7:57:30 AM

by: nareyko

[dead]

3/30/2026, 8:29:54 AM