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ICARCV
2008
IEEE

A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based animat

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A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based animat
— We investigate the use of Particle Swarm Optimization (PSO), and compare with Genetic Algorithms (GA), for a particular robot behavior-learning task: the training of an animat behavior totally determined by a fully-recurrent neural network, and with which we try to fulfill a simple exploration and food foraging task. The target behavior is simple, but the learning task is challenging because of the dynamic complexity of fully-recurrent neural networks. We show that standard PSO yield very good results for this learning problem, and appears to be much more effective than simple GA. Keywords— animat, behavior-learning, genetic algorithms, particle swarm optimization, recurrent neural network.
Fabien Moutarde
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICARCV
Authors Fabien Moutarde
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