Abstract
The engineering of living cells able to learn algorithms by themselves, such as playing board games —a classic challenge for artificial intelligence— will allow complex ecosystems and tissues to be chemically reprogrammed to learn complex decisions. However, current engineered gene circuits encoding decision-making algorithms have failed to implement self-programmability and they require supervised tuning. We show a strategy for engineering gene circuits to rewire themselves by reinforcement learning. We created a scalable general-purpose library of Escherichia coli strains encoding elementary adaptive genetic systems capable of persistently adjusting their relative levels of expression according to their previous behavior. Our strains can learn the mastery of 3×3 board games such as tic-tac-toe, even when starting from a completely ignorant state. We provide a general genetic mechanism for the autonomous learning of decisions in changeable environments.
One-Sentence Summary We propose a scalable strategy to engineer gene circuits capable of autonomously learning decision-making in complex environments.
Competing Interest Statement
The authors have declared no competing interest.
