ROIS: Role-Based Multi-Agent Collaboration by Context-Time-Aware Information Sharing

In complex cooperative tasks, Multi-Agent Reinforcement Learning (MARL) faces the dual challenges of an exponentially growing joint action space and the constraints of partial observability. While the Centralized Training with Decentralized Execution (CTDE) paradigm is widely adopted, it often leads to homogeneous policies that lack the necessary specialization for complex teamwork. While role-based methods encourage specialization, they often lack mechanisms for inter-agent interaction. Consequently, the lack of rich information for role assignment means their roles may be assigned ineffectively, hindering the convergence of the team policy to its optimum. To address this critical gap, we propose ROIS, a novel framework that enhances multi-agent collaboration by grounding dynamic role assignments in a context-time-aware information sharing mechanism. Our key insight is to leverage a dedicated information sharing module that captures multi-step temporal context, providing each agent with richer, tailored feedback from its teammates. This mechanism directly addresses the lack of inter-agent interaction, leading to more accurate and effective role assignments. This results in a more coherent task division, which guides specialized policies toward the optimal joint policy and drastically reduces ineffective exploration. We conduct extensive experiments on the demanding StarCraft II, SMACv2, and Multi-agent Particle Environment benchmarks. The results demonstrate that ROIS consistently achieves state-of-the-art performance, significantly outperforming a wide range of advanced baselines, particularly in scenarios requiring deep coordination and policy adaptation. Finally, comprehensive ablation studies confirm the essential contribution of each component to the framework’s success.