LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

• URL: http://arxiv.org/abs/2405.11106v1
• Date: Fri, 17 May 2024 22:10:23 GMT
• Title: LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
• Authors: Chuanneng Sun, Songjun Huang, Dario Pompili,
• Abstract summary: 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.
• Score: 8.55917897789612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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