Integration of reinforcement learning to realize functional variability of microfluidic systems.

In this article, we present a proof-of-concept for microfluidic systems with high functional variability using reinforcement learning. By mathematically defining the objective of tasks, we demonstrate that the system can autonomously learn to behave according to its objectives. We applied Q-learning to a peristaltic micropump and showed that two different tasks can be performed on the same platform: adjusting the flow rate of the pump and manipulating the position of the particles. First, we performed typical micropumping with flow rate control. In this task, the system is rewarded according to the deviation between the average flow rate generated by the micropump and the target value. Therefore, the objective of the system is to maintain the target flow rate via an operation of the pump. Next, we demonstrate the micromanipulation of a small object (microbead) on the same platform. The objective was to manipulate the microbead position to the target area, and the system was rewarded for the success of the task. These results confirmed that the system learned to control the flow rate and manipulate the microbead to any randomly chosen target position. In particular, the manipulation technique is a new technology that does not require the use of structures such as wells or weirs. Therefore, this concept not only adds flexibility to the system but also contributes to the development of novel control methods to realize highly versatile microfluidic systems.

Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump.

We herein report a study on the intelligent control of microfluidic systems using reinforcement learning. Integrated microvalves are utilized to realize a variety of microfluidic functional modules, such as switching of flow pass, micropumping, and micromixing. The application of artificial intelligence to control microvalves can potentially contribute to the expansion of the versatility of microfluidic systems. As a preliminary attempt toward this motivation, we investigated the application of a reinforcement learning algorithm to microperistaltic pumps. First, we assumed a Markov property for the operation of diaphragms in the microperistaltic pump. Thereafter, components of the Markov decision process were defined for adaptation to the micropump. To acquire the pumping sequence, which maximizes the flow rate, the reward was defined as the obtained flow rate in a state transition of the microvalves. The present system successfully empirically determines the optimal sequence, which considers the physical characteristics of the components of the system that the authors did not recognize. Therefore, it was proved that reinforcement learning could be applied to microperistaltic pumps and is promising for the operation of larger and more complex microsystems.