用视觉语言模型实现多智能体闭环协作，提升决策效率与可解释性。
Closed-Loop Vision-Language Planning for Multi-Agent Coordination
- 基于视觉语言模型生成可解释的代码化策略，动态更新技能库。
- 在SMACv2对称5v5任务中胜率57%，领先QMIX 30个百分点。
- 适合需要高可解释性与协同决策的多智能体系统研究者。
合作式多智能体强化学习（MARL）面临样本效率低、可解释性差和泛化能力弱的问题。尽管大语言模型（LLMs）具备强大规划能力，但其应用受限于仅依赖文本输入，且难以处理多智能体任务中非马尔可夫、部分可观测的特性。本文提出COMPASS框架，通过集成视觉语言模型（VLMs）实现去中心化、闭环决策。该框架动态生成并优化可解释的代码化策略，存入由专家演示预训练的技能库。为确保稳健协作，采用结构化多跳通信协议传递实体信息，使团队能基于局部观测构建一致认知。在挑战性SMACv2基准上评估，COMPASS显著优于现有MARL基线。尤其在对称的Protoss 5v5任务中，达到57%胜率，比QMIX（27%）高出30个百分点。
原文摘要 · Abstract (English)
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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