ODIL, which optimizes a discrete loss combining ODE residuals and travel time with a neural-network policy, solves microfluidic navigation benchmarks with one to three orders of magnitude fewer policy evaluations than reinforcement learning and remains robust in high-dimensional action spaces.
Light-driven micro-and nanomotors for envi- ronmental remediation.Environmental Science: Nano, 4(8):1602–1616, 2017
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Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss
ODIL, which optimizes a discrete loss combining ODE residuals and travel time with a neural-network policy, solves microfluidic navigation benchmarks with one to three orders of magnitude fewer policy evaluations than reinforcement learning and remains robust in high-dimensional action spaces.