I'd love to read more. Do you know any sources?

Try Reinforcement Learning for Combinatorial Optimization: A Survey

or a more recent and spirited discussion on reddit..

https://old.reddit.com/r/reinforcementlearning/comments/196i...

Link for those who need it:

https://arxiv.org/abs/2003.03600

Mm theres one purely based on position analysis and not tree search that has GM level in chess. From deepmind

SAT appears to be rather impervious to statistical methods because it is very non-linear and non-local (besides, of course, being NP-complete). Flip one single literal anywhere and poof!, your formula mght go from SAT to UNSAT, or vice versa, or you might enable a ton of simplifications, etc. So it might be very hard to train a network on such a riotous state space.

Additionally, in practice SAT solvers are also supposed to provide witnesses (i.e., a satisfying assignment or an unsatisfiability core) to back their verdicts, and that may be an ever taller order for ML.

Since all of these combinatorial optimization problems are, in full generality, are as difficult as possible in the complexity theoretic sense, there is little prospect of AI/ML helping to solve these problems in general. (?)

But people hope that some subclass of "real-life" instances of one of these optimization problems has enough structure that AI/ML may be able to learn useful heuristics for it. (This is not my field. This is just my take-away from reading some AI/ML-for-CO papers.)