“School of Nano-Sciences”
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| Paper IPM / Nano-Sciences / 18141 |
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| Abstract: | |||||
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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 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.
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