Smart navigation through a rotating barrier: Deep reinforcement learning with application to size-based separation of active microagents

Abstract

We employ deep reinforcement learning methods to investigate shortest-time navigation strategies for smart active Brownian particles (microagents), which self-propel through a rotating potential barrier in a static, viscous, fluid background. The microagent's motion begins at a specified origin and terminates at a designated destination. The potential barrier is modeled as a localized, repulsive Gaussian potential with finite support, whose peak location rotates at a given angular velocity about a fixed center within the plane of motion. We use the Advantage Actor-Critic approach to train microagents for their origin-to-destination navigation through the barrier. By employing this approach, we demonstrate that the rotating potential (as opposed to a static one) enables size-based sorting and separation of the microagents. In other words, microagents of different radii arrive at the destination at sufficiently well-separated average times, facilitating their sorting. The efficiency of particle sorting is quantified by introducing specific separation measures. We also demonstrate how training the microagents in a noisy background, as opposed to a noise-free one, can improve the precision of their size-based sorting. Our findings suggest promising avenues for future research on smart active particles equipped with deep reinforcement learning to navigate complex environments, particularly in microscale applications.