Reinforcement Learning for Dynamic Microfluidic Control.

Area of Science:

• Microfluidics
• Chemical Sciences
• Biological Sciences

Background:

• Microfluidic systems offer advantages for chemical and biological research but suffer from performance inconsistencies over time.
• Factors like channel fouling, deformation, and environmental fluctuations cause these performance issues.

Purpose of the Study:

• To investigate the use of advanced control algorithms to enhance the long-term stability and repeatability of microfluidic platforms.
• To demonstrate the efficacy of reinforcement learning for automated microfluidic experimentation.

Main Methods:

• Application of two reinforcement learning algorithms: Deep Q-Networks and model-free episodic controllers.
• Testing algorithms on both continuous-flow and segmented-flow microfluidic experimental challenges.

Main Results:

• The applied reinforcement learning algorithms achieved superhuman performance in controlling microfluidic experiments.
• Demonstrated mitigation of performance inconsistencies in microfluidic systems.

Conclusions:

• Novel control algorithms, particularly reinforcement learning, are highly effective for automated, high-throughput microfluidic experimentation.
• These algorithms enable robust and repeatable long-term experiments, overcoming common microfluidic limitations.