This is a fairly well represented technique in bayesian hyperparameter optimization, where you train a meta-classifier that keeps track of the parameter space. Kind of like a manager model, if you will, that learns to intelligently optimize exploration vs exploitation so that a team of workers will arrive at the global optimum.

Back when Yahoo! was a real company they used a technique called "multi-armed bandits" to learn what ads to show. [1]

More recently, there's a number of off-the-shelf packages available that you can trivially integrate into your ML pipeline to optimize hyperparameters of your models, I'll include the links below.

[1] multi-armed bandits https://www.theregister.co.uk/2011/09/23/yahoo_core_personal...

[2] tree of parzen estimators https://jaberg.github.io/hyperopt/

[3] hyperband - what google uses in their internal ML toolkits AFAIK https://arxiv.org/pdf/1603.06560.pdf

[4] (shameless plug) gaussian process based hyperparameter optimization service https://github.com/avantoss/loop