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- W2767600434 abstract "Through natural evolution, nervous systems of organisms formed near-optimal structures to express behavior. Here, we propose an effective way to create control agents, by textit{re-purposing} the function of biological neural circuit models, to govern similar real world applications. We model the tap-withdrawal (TW) neural circuit of the nematode, textit{C. elegans}, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neural parameters as a policy for controlling the inverted pendulum problem. For reconfiguration of the purpose of the TW neural circuit, we manipulate a search-based reinforcement learning. We show that our neural policy performs as good as existing traditional control theory and machine learning approaches. A video demonstration of the performance of our method can be accessed at url{this https URL}." @default.
- W2767600434 created "2017-11-17" @default.
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- W2767600434 date "2017-11-09" @default.
- W2767600434 modified "2023-09-27" @default.
- W2767600434 title "Worm-level Control through Search-based Reinforcement Learning." @default.
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