Cooperative Multi-Agent Planning with Adaptive Skill Synthesis

• URL: http://arxiv.org/abs/2502.10148v1
• Date: Fri, 14 Feb 2025 13:23:18 GMT
• Title: Cooperative Multi-Agent Planning with Adaptive Skill Synthesis
• Authors: Zhiyuan Li, Wenshuai Zhao, Joni Pajarinen,
• Abstract summary: Multi-agent systems with reinforcement learning face challenges in sample efficiency, interpretability, and transferability. We present a novel multi-agent architecture that integrates vision-language models (VLMs) with a dynamic skill library and structured communication for decentralized closed-loop decision-making.
• Score: 16.228784877899976
• License:
• Abstract: Despite much progress in training distributed artificial intelligence (AI), building cooperative multi-agent systems with multi-agent reinforcement learning (MARL) faces challenges in sample efficiency, interpretability, and transferability. Unlike traditional learning-based methods that require extensive interaction with the environment, large language models (LLMs) demonstrate remarkable capabilities in zero-shot planning and complex reasoning. However, existing LLM-based approaches heavily rely on text-based observations and struggle with the non-Markovian nature of multi-agent interactions under partial observability. We present COMPASS, a novel multi-agent architecture that integrates vision-language models (VLMs) with a dynamic skill library and structured communication for decentralized closed-loop decision-making. The skill library, bootstrapped from demonstrations, evolves via planner-guided tasks to enable adaptive strategies. COMPASS propagates entity information through multi-hop communication under partial observability. Evaluations on the improved StarCraft Multi-Agent Challenge (SMACv2) demonstrate COMPASS achieves up to 30\% higher win rates than state-of-the-art MARL algorithms in symmetric scenarios.

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