@inproceedings{5ef182cce4b841b8becf8867fa267bbd,
title = "Distributed safe reinforcement learning for multi-robot motion planning",
abstract = "This paper studies optimal motion planning of multiple mobile robots with collision avoidance. We develop a distributed reinforcement learning algorithm which ensures suboptimal goal reaching and anytime collision avoidance simultaneously. Theoretical results on the convergence of neural network weights, the uniform and ultimate boundedness of system states of the closed-loop system, and anytime collision avoidance are established. Numerical simulations for single integrator and unicycle robots illustrate the effectiveness of our theoretical results.",
author = "Yang Lu and Yaohua Guo and Guoxiang Zhao and Minghui Zhu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th Mediterranean Conference on Control and Automation, MED 2021 ; Conference date: 22-06-2021 Through 25-06-2021",
year = "2021",
month = jun,
day = "22",
doi = "10.1109/MED51440.2021.9480176",
language = "English (US)",
series = "2021 29th Mediterranean Conference on Control and Automation, MED 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1209--1214",
booktitle = "2021 29th Mediterranean Conference on Control and Automation, MED 2021",
address = "United States",
}