Application of Reinforcement Learning to Realize Highly Flexible Microsystem

Abstract

We herein report on our recent investigation on the development of flexible control of micro (fluidic) system by the application of machine learning (reinforcement learning). Classical peristaltic micropump is employed as platform of the studies and Q-learning algorism, which is also classical algorism of reinforcement learning, is applied to the system. The acquiring of optimal micropumping behavior and manipulation of microbead in microchannel are demonstrated on the platform. The acquired micropumping sequence realize higher flow rate than typical sequences proposed in earlier studies. It is understood that the unique characteristics of the system are considered to acquire the sequence. The efficient micromanipulation of the microbead is also demonstrated on the same platform, even the microdevice is originally designed for the micropumping. Therefore, it could be concluded that the application of the reinforcement learning to a microsystem could be effective to extend the versatility by bringing out the potential of the system.