LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
<|reference_start|>LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions: 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.<|reference_end|>
@article{sun2024llm-based,
    title={LLM-based Multi-Agent Reinforcement Learning: Current and Future
  Directions},
    author={Chuanneng Sun, Songjun Huang, Dario Pompili},
    journal={arXiv preprint arXiv:2405.11106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11106},
    primaryClass={cs.MA cs.AI cs.CL cs.LG cs.RO}
}