Reinforcement learning with a single microswimmer

Santiago Muiños-Landin; Alexander Fischer; Viktor Holubec; Frank Cichos

doi:10.1117/12.2568456

20 August 2020 Reinforcement learning with a single microswimmer

Santiago Muiños-Landin, Alexander Fischer, Viktor Holubec, Frank Cichos

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690Z (2020) https://doi.org/10.1117/12.2568456
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Santiago Muiños-Landin, Alexander Fischer, Viktor Holubec, and Frank Cichos "Reinforcement learning with a single microswimmer", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690Z (20 August 2020); https://doi.org/10.1117/12.2568456

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