# Prospects of reinforcement learning for the simultaneous damping of many mechanical modes

## TL;DR

This paper explores using reinforcement learning to develop adaptive feedback strategies for cooling multiple mechanical vibrational modes simultaneously, demonstrating effective reduction of system temperature through neural network policies.

## Contribution

It introduces a neural network-based reinforcement learning approach for designing feedback control to cool several mechanical modes at once, a novel application in optomechanics.

## Key findings

- Successful numerical demonstration of cooling four independent modes
- Significant reduction in total system temperature
- Effective control via optical modulations and radiation pressure

## 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 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.