An amortized reinforcement learning method enables immediate, observation-driven sequential optimization of genetic circuits while accounting for both intrinsic stochasticity and cross-laboratory variability without repeated inference steps.
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Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
An amortized reinforcement learning method enables immediate, observation-driven sequential optimization of genetic circuits while accounting for both intrinsic stochasticity and cross-laboratory variability without repeated inference steps.