Deep reinforcement learning for feedback control in a collective flashing ratchet

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A collective flashing ratchet transports Brownian particles using a spatially periodic, asymmetric, and time-dependent on-off switchable potential. The net current of the particles in this system can be substantially increased by feedback control based on the particle positions. Several feedback policies for maximizing the current have been proposed, but optimal policies have not been found for a moderate number of particles. Here, we use deep reinforcement learning (RL) to find optimal policies, with results showing that policies built with a suitable neural network architecture outperform the previous policies. Moreover, even in a time-delayed feedback situation where the on-off switching of the potential is delayed, we demonstrate that the policies provided by deep RL provide higher currents than the previous strategies.
Publisher
AMER PHYSICAL SOC
Issue Date
2021-04
Language
English
Article Type
Article
Citation

PHYSICAL REVIEW RESEARCH, v.3, no.2

ISSN
2643-1564
DOI
10.1103/PhysRevResearch.3.L022002
URI
http://hdl.handle.net/10203/282791
Appears in Collection
PH-Journal Papers(저널논문)
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