Artificial microswimmers are designed to mimic the self-propulsion of microscopic living organisms to yield access to the complex behavior of active matter. As compared to their living counterparts, they have only limited ability to adapt to environmental signals or retain a physical memory. Yet, different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to thermal noise as a key feature in microscopic systems.
Here we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a standard navigation problem with single and multiple swimmers and show that noise decreases the learning speed, increases the decision strength and modifies the optimal behavior based on a delayed response in the noisy environment.
Description of stochastic motion of a particle in an unstable potential is a challenging topic since even small number of diverging trajectories leads to undefined statistic moments of particle position. This breaks down the standard statistical analysis of unstable mechanical processes and their applications. Therefore, we employ a different approach taking advantage of the local characteristics of the most-likely particle motion instead of the average motion. We experimentally verify theoretical predictions for a Brownian particle moving near an inflection in a cubic optical potential. Notably, the most-likely position of the particle atypically shifts against the force despite the trajectories diverge in opposite direction. In this work we study the influence of the analytical formula used for quantification of the most likely particle position parameters in the case where only limited number of trajectories is available.
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