2021/09/20 by Jingqing Ruan, Linghui Meng, Ruan, Jingqing +7
Computer Science · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Human Pose and Action Recognition #Multiagent Systems (cs.MA) #Multimodal Machine Learning Applications #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.2110.08126
openalex publication_date 2021/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously, and asynchronous action coordination between agents is rarely considered. This paper introduces a bi-level multi-agent decision hierarchy for coordinated behavior planning. We propose a novel election mechanism in which we adopt a graph convolutional network to model the interaction among agents and elect a first-move agent for asynchronous guidance. We also propose a dynamically weighted mixing network to effectively reduce the misestimation of the value function during training. This work is the first to explicitly model the asynchronous multi-agent action coordination, and this explicitness enables to choose the optimal first-move agent. The results on Cooperative Navigation and Google Football demonstrate that the proposed algorithm can achieve superior performance in cooperative environments. Our code is available at \urlhttps://github.com/Amanda-1997/EFA-DWM.