A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits

Author
Abstract

<p>We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level.</p>

Year of Publication

2020
Conference Name

Proceedings of the 37th International Conference on Machine Learning
Date Published

13–18 Jul
Publisher

PMLR
URL

http://proceedings.mlr.press/v119/hasani20a.html
Download citation