How can LLMs learn to communicate effectively in a multi-agent game?
The Signaler-Responder Game: Learning to Communicate using Thompson Sampling
October 29, 2024
https://arxiv.org/pdf/2410.19962This paper investigates how two AI agents, a "signaler" and a "responder," can learn to communicate and cooperate effectively without pre-programmed instructions. They design a game where the signaler must decide when to signal based on its needs, and the responder must decide how to react.
The key finding relevant to LLM-based multi-agent systems is that the proposed algorithm, based on a concept called Thompson Sampling, allows these agents to learn sophisticated communication strategies solely through repeated interaction. This demonstrates how LLMs could power agents that learn to communicate and cooperate autonomously, leading to more efficient and adaptive multi-agent applications.