Publication

Particle swarm optimization for unsupervised robotic learning

Alcherio Martinoli
2005
Conference Papers

Abstract

We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate the performance of both the original algorithmand the noise-resistantmethod for several numerical problems with added noise, as well as unsupervised learning of obstacle avoidance using one or more robots.

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Particle swarm optimization for unsupervised robotic learning

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