  Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless
collaboration among agents, often represented by an underlying relation graph.
Existing methods for learning this graph primarily focus on agent-pair
relations, neglecting higher-order relationships. While several approaches
attempt to extend cooperation modelling to encompass behaviour similarities
within groups, they commonly fall short in concurrently learning the latent
graph, thereby constraining the information exchange among partially observed
agents. To overcome these limitations, we present a novel approach to infer the
Group-Aware Coordination Graph (GACG), which is designed to capture both the
cooperation between agent pairs based on current observations and group-level
dependencies from behaviour patterns observed across trajectories. This graph
is further used in graph convolution for information exchange between agents
during decision-making. To further ensure behavioural consistency among agents
within the same group, we introduce a group distance loss, which promotes group
cohesion and encourages specialization between groups. Our evaluations,
conducted on StarCraft II micromanagement tasks, demonstrate GACG's superior
performance. An ablation study further provides experimental evidence of the
effectiveness of each component of our method.
