Deep reinforcement learning for feedback control in a collective flashing ratchet
This work provides improved feedback control policies for enhancing particle transport in flashing ratchet systems, which is relevant for researchers working on microscopic transport and active matter.
This paper addresses the problem of maximizing particle current in a collective flashing ratchet system using feedback control. The authors employ deep reinforcement learning to find optimal feedback policies, demonstrating that their neural network-based policies outperform previously proposed strategies, even in scenarios with time-delayed feedback.
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.