Global-Localized Agent Graph Convolution for Multi-Agent Reinforcement Learning
Yuntao Liu, Yong Dou, Siqi Shen, Peng Qiao
Abstract
A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It is a challenging problem to enable agent to learn global and localized cooperate information simultaneously in multi-agent systems. In this paper, we model the global and localized cooperation among agents by global and localized agent graphs and propose a novel graph convolutional reinforcement learning mechanism based on these two graphs which allows each agent to communicate with neighbors and all a-gents to cooperate at the high level. Experiments on the large-scale multi-agent scenarios in StarCraft II show that our pro-posed method gets better performance compared with state-of-the-art algorithms and allows agents learning to cooperate efficiently.
BibTeX
@inproceedings{icassp2021_globallocalizeda,
title = {Global-Localized Agent Graph Convolution for Multi-Agent Reinforcement Learning},
author = {Yuntao Liu and Yong Dou and Siqi Shen and Peng Qiao},
booktitle = {ICASSP 2021},
year = {2021}
}