Closed-Loop Vision-Language Planning for Multi-Agent Coordination

Zhiyuan Li; Wenshuai Zhao; Joni Pajarinen

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arXiv · 2502.10148

Closed-Loop Vision-Language Planning for Multi-Agent Coordination

Zhiyuan Li·Wenshuai Zhao·Joni Pajarinen

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Abstract

Cooperative multi-agent reinforcement learning (MARL) struggles with sample efficiency, interpretability, and generalization. While Large Language Models (LLMs) offer powerful planning capabilities, their application has been hampered by a reliance on text-only inputs and a failure to handle the non-Markovian, partially observable nature of multi-agent tasks. We introduce COMPASS, a multi-agent framework that overcomes these limitations by integrating Vision-Language Models (VLMs) for decentralized, closed-loop decision-making. COMPASS dynamically generates and refines interpretable, code-based strategies stored in a skill library that is bootstrapped from expert demonstrations. To ensure robust coordination, it propagates entity information through a structured multi-hop communication protocol, allowing teams to build a coherent understanding from partial observations. Evaluated on the challenging SMACv2 benchmark, COMPASS significantly outperforms state-of-the-art MARL baselines. Notably, in the symmetric Protoss 5v5 task, COMPASS achieved a 57\% win rate, a 30 percentage point advantage over QMIX (27\%). Project page can be found at https://stellar-entremet-1720bb.netlify.app/.

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Zhiyuan Li, Wenshuai Zhao, Joni Pajarinen. 2025-02-14. Closed-Loop Vision-Language Planning for Multi-Agent Coordination. https://arxiv.org/abs/2502.10148

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