How can I make AI agents collaborate despite communication delays?
CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness
This paper addresses the challenge of communication delays in multi-agent reinforcement learning (MARL), where agents receive messages from different time points (asynchronous communication). Existing MARL systems often assume instant communication, which is unrealistic in real-world applications.
For LLM-based multi-agent systems, this research is crucial because LLMs rely heavily on message passing for coordination. The proposed "CoDe" framework introduces two key ideas: 1) Agents learn to communicate "intents" representing future behavior trends, making messages more robust to delays. 2) A dual alignment mechanism considers both the intent and the timeliness of messages during fusion, allowing agents to selectively utilize information from delayed messages. This is directly relevant to LLM agents interacting in real-time, where delays are inevitable and effective communication is crucial for collaborative performance.