What are the right algorithms for this open loop control problem

i’m quiete new to AI, i have to choose the right algorithm for a machine learning problem. Since there are so many methods, algorithms and techniques, i’m not sure whether i’m on the right track.

The problem we want to tackle with machine learning goes something like this:
For each process we execute we get a certain performance. In each process our tool wears, which has an (unknown) effect on our performance. We also can adjust five parameters of our machine, which also have an (unknown) effect on our performance. we want to adjust the five parameters of our machine in a way, that performance is constant, even though tool wear is happening.

For learning, we would execute our process with different wear and machine parameter configurations and see how performance reacts. We will be able to execute quiete a big amount of experiments (500-1000)

To begin with, we assume that there are 20 different tool wear states (from sharp till dull), we have five parameters of the machine we can freely adjust.

To my understanding so far, this is an open loop control problem. We could use reinforcement learning to learn the relation of tool wear and machine parameters for a certain desired machine performance.

My questions:
Is reinforcement learning the correct method?
Which algorithm is used to identify the next machine parameter set for the next experiment in learning phase?
Which learning algorithm should i choose?

Any hints, tutorials, correct methods, links which help would be appreciated.

thanks+regards