论文标题
集体闪烁的棘轮中的反馈控制深度加固学习
Deep reinforcement learning for feedback control in a collective flashing ratchet
论文作者
论文摘要
集体闪烁的棘轮使用空间周期性，不对称和时间依赖的开关切换电势将布朗颗粒传输。该系统中粒子的净电流可以通过基于粒子位置的反馈控制大大增加。已经提出了几种用于最大化电流的反馈策略，但是对于中等数量的颗粒，尚未发现最佳策略。在这里，我们使用深度加固学习（RL）来查找最佳政策，结果表明，使用合适的神经网络体系结构构建的政策优于先前的策略。此外，即使在延迟的反馈情况下，电势的开关转换被延迟，我们也证明，深RL提供的政策提供了比以前的策略更高的电流。
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.
