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.
Compu- tational oncology—mathematical modelling of drug regimens for precision medicine.Nature reviews Clinical oncology, 13(4):242–254, 2016
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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.