Egalitarian Social Learning (ESL) in Robot Foraging (2012)
Author: Wesley Tansey
This is a demo of robot foraging using Neuroevolution (specifically NEAT) with and without online Egalitarian Social Learning (ESL). The agents begin with randomly initialized networks, resulting in poor starting behaviors. Without ESL, they have fixed policies during each generation and do not improve during the episode. However, ESL agents are able to learn from observations of others in the population and quickly improve their policy. Thus, ESL agents begin to exhibit good behaviors before even the first generation has completed.

NEAT only
Wesley Tansey Formerly affiliated Collaborator tansey [at] cs utexas edu
Eliana Feasley Formerly affiliated Ph.D. Student elie [at] cs utexas edu
Accelerating Evolution via Egalitarian Social Learning 2012
Wesley Tansey, Eliana Feasley, and Risto Miikkulainen, In Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, Pennsylvania, USA 2012.
ESL This is the C# source code for the experiments with Egalitarian Social Learning (ESL) in a robot foraging domain. The re... 2012