Abstract
Reinforcement Learning (RL) is employed to develop control techniques for manipulating acoustic cavitation bubbles. This paper presents a proof of concept in which an RL agent is trained to discover a policy that allows precise control of bubble positions within a dual-frequency standing acoustic wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications.