GenCircuit-RL uses hierarchical verification rewards and curriculum learning in RL to generate correct genetic circuit code in SBOL, improving functional task success by 14-16 points and generalizing to novel biological parts.
Distributed biological computation with multicellular engineered networks
2 Pith papers cite this work, alongside 361 external citations. Polarity classification is still indexing.
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Direct competition enables high-probability majority consensus in microbial populations for initial gaps Omega(sqrt(n log n)), while its absence requires Omega(n) gaps for constant probability.
citing papers explorer
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GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
GenCircuit-RL uses hierarchical verification rewards and curriculum learning in RL to generate correct genetic circuit code in SBOL, improving functional task success by 14-16 points and generalizing to novel biological parts.
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Reaching Agreement in Competitive Microbial Systems
Direct competition enables high-probability majority consensus in microbial populations for initial gaps Omega(sqrt(n log n)), while its absence requires Omega(n) gaps for constant probability.