How can agents cooperate with limited info?
Networked Agents in the Dark: Team Value Learning under Partial Observability
This paper introduces DNA-MARL, a novel approach for training cooperative multi-agent systems in scenarios where agents have limited or "partial" views of the overall situation. Instead of relying on a central controller or full information sharing, agents communicate locally to build a shared understanding of the team's goals and learn how to best contribute to them.
Key points for LLM-based multi-agent systems: DNA-MARL facilitates decentralized training and execution, addressing the practical challenges of building large-scale multi-agent systems. Its consensus mechanism for shared understanding is particularly relevant to LLM agents, offering a potential way for them to align their actions without direct access to each other's internal states or requiring excessive communication. This is particularly relevant for privacy-preserving scenarios where sharing full observations might not be feasible. The flexible degree of cooperation offered by DNA-MARL allows controlling the trade-off between individual and collective behavior in LLM-based agents.