@inproceedings{adadef840ced43c3bd5ae13baf4745fa,
title = "Parameter Optimization via Reinforcement Learning for the Regulation of Swarms",
abstract = "The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space.",
keywords = "boids model, maximum coverage, obstacle avoidance, q-learning",
author = "Qizhen Wu and Gaoxiang Liu and Kexin Liu and Lei Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023 ; Conference date: 02-07-2023 Through 04-07-2023",
year = "2023",
doi = "10.1109/ICCSS58421.2023.10270800",
language = "English",
series = "2023 9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "62--67",
booktitle = "2023 9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023",
address = "United States",
}