  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.
