Review of unmanned cluster routing protocols based on deep reinforcement learning

Jia-Xin Peng; Lin-Feng Yuan; Sheng Liu; Qin Zhang

doi:10.1117/12.2673817

6 April 2023 Review of unmanned cluster routing protocols based on deep reinforcement learning

Jia-Xin Peng, Lin-Feng Yuan, Sheng Liu, Qin Zhang

Proceedings Volume 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022); 126152B (2023) https://doi.org/10.1117/12.2673817
Event: International Conference on Signal Processing and Communication Technology (SPCT 2022), 2022, Harbin, China

Abstract

Most unmanned clusters are typical mobile self-organizing networks (MANETs) with high dynamics and energy constraints. Deep reinforcement learning (DRL) is an emerging hotspot in the field of machine learning because it can make decisions without relying on models and is very suitable for dynamic unmanned cluster communication systems with time-varying network conditions. DRL -based unmanned cluster routing protocols are of great research value and significance. This paper introduces the fundamental characteristics of unmanned clusters and dynamic routing protocols; briefly describes the fundamental elements of DRL algorithms and their applicability in dynamic routing protocols; reviews the applications of DRL algorithms in unmanned clusters routing protocols; and summarizes the challenges and future development directions of unmanned clusters routing protocols based on DRL.

Citation Download Citation

Jia-Xin Peng, Lin-Feng Yuan, Sheng Liu, and Qin Zhang "Review of unmanned cluster routing protocols based on deep reinforcement learning", Proc. SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), 126152B (6 April 2023); https://doi.org/10.1117/12.2673817

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