VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning
Qian Zhang, Zhuo Sun, Yao Zhang, Zhiwen Yu, Bin Guo, Jun Zhang
Abstract
Inter-agent communication serves as an effective mechanism for enhancing performance in collaborative multi-agent reinforcement learning (MARL) systems. However, the inherent communication latency in practical systems induces both action decision delays and outdated information sharing, impeding MARL performance gains, particularly in time-critical applications like autonomous driving. In this work, we propose a Value-of-Information aware Low-latency Communication (VIL2C) scheme that proactively adjusts the latency distribution to mitigate its effects in MARL systems. Specifically, we define a Value of Information (VoI) metric to quantify the importance of delayed messages on the recipient agent
BibTeX
@inproceedings{aaai2026_vil2cvalueofinfo,
title = {VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning},
author = {Qian Zhang and Zhuo Sun and Yao Zhang and Zhiwen Yu and Bin Guo and Jun Zhang},
booktitle = {AAAI 2026},
year = {2026}
}