In a simulated low-Reynolds-number swimmer with 2 to 4 rigid paddle pairs, reinforcement learning recovers the biologically common back-to-front metachronal wave as the most efficient stroke, while front-to-back or paired strokes can be faster at wide spacings.
In: Physics and Our World: Reissue of the Proceedings of a Symposium in Honor of Vic- tor F Weisskopf, pp
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Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number
In a simulated low-Reynolds-number swimmer with 2 to 4 rigid paddle pairs, reinforcement learning recovers the biologically common back-to-front metachronal wave as the most efficient stroke, while front-to-back or paired strokes can be faster at wide spacings.