AI RESEARCH
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
arXiv CS.LG
•
ArXi:2403.19253v3 Announce Type: replace Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. They rely solely on one-step observations, neglecting crucial historical experiences, leading to deficient graphs that foster redundant or detrimental information exchanges. Additionally, high computational demands for action-pair calculations in dense graphs impede scalability.