1. Evolutionary vs imitation learning for neuromorphic control at the edge*Notice: this manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Issue 1 (24th January 2022) Authors: Schuman, Catherine; Patton, Robert; Kulkarni, Shruti; Parsa, Maryam; Stahl, Christopher; Haas, N Quentin; Mitchell, J Parker; Snyder, Shay; Nagle, Amelie; Shanafield, Alexandra; Potok, Thomas Journal: Neuromorphic computing and engineering Issue: Volume 2:Issue 1(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗