@inproceedings{7ded30f738a445b7b26bfd5c4aaf92c8,
title = "A Deep Reinforcement Learning Based Leader-Follower Control Policy for Swarm Systems",
abstract = "This paper is concerned with the learning-based control problem for large-scale robotic swarm systems, which makes the single leader able to herd the follower swarm systems to form a target distribution. We use the mean-field model to describe the spatio-temporal evolution of the probability density of the follower swarm, under which the physical space is divided into several bins and the leader control policy only depends on the density distribution over these bins. Therefore, the designed control policy is free from the computation issue raised by the large number of follower agents N. A deep reinforcement learning (DRL) algorithm is designed here to learn the leader control policy and accommodate the variation of the follower density. It is verified that the proposed control policy is much more efficient than existing results in terms of control performance and training time.",
keywords = "Deep reinforcement learning (DRL), Leader-follower control, Mean-field model, Swarm systems",
author = "Di Cui and Huiping Li and Rizhong Wang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th China Conference on Intelligent Networked Things, CINT 2022 ; Conference date: 07-08-2022 Through 08-08-2022",
year = "2022",
doi = "10.1007/978-981-19-8915-5\_23",
language = "英语",
isbn = "9789811989148",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "269--280",
editor = "Lin Zhang and Wensheng Yu and Haijun Jiang and Yuanjun Laili",
booktitle = "Intelligent Networked Things - 5th China Conference, CINT 2022, Revised Selected Papers",
}