In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poem writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Reinforcement Learning (RL) and achieve decent results, the extension of LLM-based RL to Multi-Agent System (MAS) is not trivial, as many aspects, such as coordination and communication between agents, are not considered in the RL frameworks of a single agent. To inspire more research on LLM-based MARL, in this letter, we survey the existing LLM-based single-agent and multi-agent RL frameworks and provide potential research directions for future research. In particular, we focus on the cooperative tasks of multiple agents with a common goal and communication among them. We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.

翻译：近年来，大型语言模型在问答、算术问题求解和诗歌创作等多种任务中展现出卓越能力。尽管LLM作为智能体的研究表明，LLM可应用于强化学习并取得良好效果，但将其扩展到多智能体系统并非易事，因为单智能体强化学习框架未考虑智能体间的协调与通信等关键问题。为激发更多基于LLM的多智能体强化学习研究，本文综述了现有基于LLM的单智能体和多智能体强化学习框架，并提出未来潜在研究方向。我们特别关注具有共同目标的多智能体协作任务及其通信机制，同时探讨框架中语言组件支持的人机协同/在环场景。