Microfluidic devices are utilized to control and direct flow behavior in a
wide variety of applications, particularly in medical diagnostics. A
particularly popular form of microfluidics -- called inertial microfluidic flow
sculpting -- involves placing a sequence of pillars to controllably deform an
initial flow field into a desired one. Inertial flow sculpting can be formally
defined as an inverse problem, where one identifies a sequence of pillars
(chosen, with replacement, from a finite set of pillars, each of which produce
a specific transformation) whose composite transformation results in a
user-defined desired transformation. Endemic to most such problems in
engineering, inverse problems are usually quite computationally intractable,
with most traditional approaches based on search and optimization strategies.
In this paper, we pose this inverse problem as a Reinforcement Learning (RL)
problem. We train a DoubleDQN agent to learn from this environment. The results
suggest that learning is possible using a DoubleDQN model with the success
frequency reaching 90% in 200,000 episodes and the rewards converging. While
most of the results are obtained by fixing a particular target flow shape to
simplify the learning problem, we later demonstrate how to transfer the
learning of an agent based on one target shape to another, i.e. from one design
to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho
