Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a novel Group-Aware Coordination Graph (GACG) for multi-agent reinforcement learning that captures both pairwise and group-level relationships, improving cooperation and decision-making.

Contribution

The paper proposes a new method to learn a latent coordination graph that models both pairwise and group-level dependencies, enhancing multi-agent cooperation.

Findings

GACG outperforms existing methods on StarCraft II tasks.

The group distance loss promotes better group cohesion.

Ablation studies confirm the effectiveness of each component.

Abstract

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.