interface (mloop.interfaces.Interface) – The M-LOOP interface from where you obtain the cost value of the
test points.
qctrl (qctrl.Qctrl) – The object representing the Boulder Opal session. You must initiate
it before calling the controller.
optimizer (qctrl.types.closed_loop_optimization_step.Optimizer) – The Boulder Opal automated closed-loop optimizer that you want to
use. It must be a valid optimizer object (containing either an
initializer or a state from a previous optimization run), as
described in the Boulder Opal reference documentation.
test_point_count (int, optional) – The requested number of test points that the Boulder Opal automated
closed-loop optimizer generates at each step. If chosen, it must be
greater than zero. This is a hint only. The Boulder Opal automated
closed-loop optimizer might choose differently.
learner (mloop.learners.Learner, optional) – The M-LOOP Learner that this controller uses to obtain extra test
points, before there are enough results to run a step of the
Boulder Opal automated closed-loop optimizer. Defaults to None, in
which case this controller uses a RandomLearner.
training_run_count (int, optional) – The minimum number of training points that the controller obtains
before calling the Boulder Opal automated closed-loop optimizer.
Defaults to 0, in which case the controller uses the minimum number
of points that the Boulder Opal automated closed-loop optimizer
requires.
interleaved_run_count (int, optional) – The minimum number of test points that the controller obtains from
the learner between two calls of the Boulder Opal automated
closed-loop optimizer, in addition to the points that Boulder Opal
requested. Defaults to 0.
kwargs (dict) – All the extra arguments that the Controller class from M-LOOP
accepts.