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.
Physics of Fluids 26(9) (2014)
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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.