  We apply adaptive feedback for the partial refrigeration of a mechanical
resonator, i.e. with the aim to simultaneously cool the classical thermal
motion of more than one vibrational degree of freedom. The feedback is obtained
from a neural network parametrized policy trained via a reinforcement learning
strategy to choose the correct sequence of actions from a finite set in order
to simultaneously reduce the energy of many modes of vibration. The actions are
realized either as optical modulations of the spring constants in the so-called
quadratic optomechanical coupling regime or as radiation pressure induced
momentum kicks in the linear coupling regime. As a proof of principle we
numerically illustrate efficient simultaneous cooling of four independent modes
with an overall strong reduction of the total system temperature.
