  Noise generated by vortices is a sensible measure of the strength of wakes
generated in a flow field. This paper presents an innovative approach to
actively control wakes and noise generated in a flow past a cylinder using deep
reinforcement learning (DRL) as a continuously learning active control
algorithm with noise levels recorded using acoustic level around the wake
region. The two primary objectives of this study is to investigate the
feasibility and effectiveness of use of acoustic level as controlling parameter
and employing DRL algorithms to optimize the control of flow dynamics. A
hydrophone array is utilised to capture the wake pattern along the flow field
downstream of a circular cylinder in the form of acoustic signals. The
collected data are used to create a real-time feedback loop for a DRL agent to
adjust jet actuators strategically placed on the cylinder's surface. This
approach enables the agent to learn and adapt its control strategy based on the
observed acoustic feedback, resulting in a closed-loop control system. We
demonstrate that the DRL-based flow control strategy can effectively reduce
wake amplitude and the noise generated. The noise level reduces by an
appreciable 6.9\% and 9.5\% in the given setting for two control
configurations. Similarly, the drag coefficient reduces by a remarkable 15.9\%
and 23.8\% respectively. Specifically, it reduces the oscillation amplitude in
drag and noise. The proposed method offers promising results in terms of
reducing flow-induced vibration. The paper highlights the potential for using
DRL, jets, and hydrophone arrays in active flow control, opening new avenues
for optimizing flow control in practical engineering applications.
