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Abstract

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 fnite 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
efcient simultaneous cooling of four independent modes with an overall strong reduction of the total
system temperature.