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arXiv · 1803.08554

Neuronal Circuit Policies

Mathias Lechner·Ramin M. Hasani·Radu Grosu
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Abstract

We propose an effective way to create interpretable control agents, by re-purposing the function of a biological neural circuit model, to govern simulated and real world reinforcement learning (RL) test-beds. We model the tap-withdrawal (TW) neural circuit of the nematode, C. elegans, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neuronal parameters as a policy for controlling basic RL tasks. We also autonomously park a real rover robot on a pre-defined trajectory, by deploying such neuronal circuit policies learned in a simulated environment. For reconfiguration of the purpose of the TW neural circuit, we adopt a search-based RL algorithm. We show that our neuronal policies perform as good as deep neural network policies with the advantage of realizing interpretable dynamics at the cell level.

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Mathias Lechner, Ramin M. Hasani, Radu Grosu. 2018-03-22. Neuronal Circuit Policies. https://arxiv.org/abs/1803.08554

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