Can LLMs improve multi-agent planning efficiency?
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis
February 17, 2025
https://arxiv.org/pdf/2502.10148This paper introduces COMPASS, a novel framework for building cooperative multi-agent systems, particularly suited for complex, partially observable environments like StarCraft II's SMACv2. COMPASS utilizes Vision-Language Models (VLMs) to enable agents to perceive visual and textual information, reason about tasks, reflect on past actions, and select appropriate actions.
Key points for LLM-based multi-agent systems:
- Closed-loop planning: COMPASS operates in a closed-loop fashion, incorporating environmental feedback for dynamic adaptation and skill refinement, addressing the limitations of open-loop LLM-agent systems.
- Dynamic skill synthesis: A dynamic skill library, initialized with demonstrations and expanded via VLM-generated code, allows for adaptable and interpretable agent behavior, moving beyond predefined action spaces.
- Structured communication: COMPASS employs a structured, entity-based communication protocol to facilitate efficient information sharing among agents under partial observability, mitigating the risks of hallucination and irrelevant information exchange common in unstructured communication.
- Code as policy: VLMs generate Python code representing agent actions, allowing for complex and adaptable behaviors.
- Integration of perception, reasoning, and reflection: The modular architecture incorporating these cognitive components demonstrates a more sophisticated approach to LLM-based agent decision-making.
- Performance on SMACv2: Evaluations showcase COMPASS's ability to outperform traditional MARL algorithms in specific scenarios, especially those involving the Protoss race, demonstrating the potential of VLM-based agents in complex multi-agent environments.