How can agents efficiently cooperate with limited communication?
ASYNCHRONOUS COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING WITH LIMITED COMMUNICATION
This paper introduces AsynCoMARL, a new approach for coordinating multiple AI agents in scenarios with limited and asynchronous communication, inspired by real-world challenges like space exploration. It uses graph transformers to let agents learn efficient communication protocols from dynamic graphs, adapting to changes in network connectivity.
Key points for LLM-based multi-agent systems: AsynCoMARL demonstrates the potential of graph transformers for managing communication in decentralized, asynchronous multi-agent scenarios, directly applicable to web-based LLM agents needing efficient coordination despite network limitations and varying activity levels. The flexible communication protocol learned by the agents could be adapted for exchanging information relevant to LLMs, like partial results, queries, or context updates. The dynamic graph representation accommodates agent availability and communication constraints typical in web environments. The emphasis on reward structure design offers insights for training LLM-based agents to collaborate effectively while minimizing communication overhead.