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Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution.

Authors :
Iwami, Arumu
Morimoto, Daichi
Shiozaki, Naoya
Hiraga, Motoaki
Ohkura, Kazuhiro
Source :
Artificial Life & Robotics; Nov2023, Vol. 28 Issue 4, p669-679, 11p
Publication Year :
2023

Abstract

This paper focuses on generating collective behavior of a robotic swarm using an attention agent. The selective attention mechanism enables an agent to cope with environmental variations which are irrelevant to the task. This paper applies attention mechanisms to a robotic swarm for enhancing system-level properties, such as flexibility or scalability. To train an attention agent, evolutionary computations become a promising method, because a controller structure is not restricted by a gradient-based method. Therefore, this paper employs a deep neuroevolution approach to generating collective behavior in a robotic swarm. The experiments are conducted by computer simulations that consist of the Unity 3D game engine. The performance of the attention agent is compared with the convolutional neural network approach. The experimental results showed that the attention agent obtained generalization abilities in a robotic swarm similar to single-agent problems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14335298
Volume :
28
Issue :
4
Database :
Complementary Index
Journal :
Artificial Life & Robotics
Publication Type :
Academic Journal
Accession number :
173431859
Full Text :
https://doi.org/10.1007/s10015-023-00902-x