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

Abstract : 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.
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Communication dans un congrès
10th International Conference on Control, Automation, Robotics and Vision (ICARCV 2008), Dec 2008, Hanoï, Vietnam. 2008
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https://hal.inria.fr/inria-00332104
Contributeur : Fabien Moutarde <>
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Dernière modification le : mardi 27 mars 2018 - 16:06:18
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Fabien Moutarde. A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based Animat. 10th International Conference on Control, Automation, Robotics and Vision (ICARCV 2008), Dec 2008, Hanoï, Vietnam. 2008. 〈inria-00332104〉

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