Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy
<|reference_start|>Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy: Diplomacy is one of the most sophisticated activities in human society. The complex interactions among multiple parties/ agents involve various abilities like social reasoning, negotiation arts, and long-term strategy planning. Previous AI agents surely have proved their capability of handling multi-step games and larger action spaces on tasks involving multiple agents. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. Recently, LLM agents have shown their potential for extending the boundary of previous agents on a couple of applications, however, it is still not enough to handle a very long planning period in a complex multi-agent environment. Empowered with cutting-edge LLM technology, we make the first stab to explore AI's upper bound towards a human-like agent for such a highly comprehensive multi-agent mission by combining three core and essential capabilities for stronger LLM-based societal agents: 1) strategic planner with memory and reflection; 2) goal-oriented negotiate with social reasoning; 3) augmenting memory by self-play games to self-evolving without any human in the loop.<|reference_end|>
@article{guan2024richelieu:,
    title={Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy},
    author={Zhenyu Guan and Xiangyu Kong and Fangwei Zhong and Yizhou Wang},
    journal={NuerIPS 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06813},
    primaryClass={cs.AI cs.MA cs.SI}
}