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[Illinois]: Perturbative Reinforcement Learning Using Directed Drift

By AbderRahman N Sobh1, Jessica S Johnson1, NanoBio Node1

1. University of Illinois at Urbana-Champaign

This tool trains two-layered networks of sigmoidal units to associate patterns using a real-valued adaptation of the directed drift algorithm.

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Version 1.0d - published on 06 Aug 2014

doi:10.4231/D39P2W68W cite this

Open source: license | download

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World usage

Location of all "[Illinois]: Perturbative Reinforcement Learning Using Directed Drift" Users Since Its Posting

Simulation Users


4 4 4 4 4 4 4 4 4 4

Users By Organization Type
Type Users
Educational - University 3 (75%)
Educational - Pre-College 1 (25%)
Users by Country of Residence
Country Users
us UNITED STATES 4 (100%)

Simulation Runs


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Average Total
Wall Clock Time 1.28 minutes 8.98 minutes
CPU time 1.23 seconds 8.64 seconds
Interaction Time 27.34 seconds 3.19 minutes, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.