Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search
<|reference_start|>Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search: In this paper, we propose a new method Strategist that utilizes LLMs to acquire new skills for playing multi-agent games through a self-improvement process. Our method gathers quality feedback through self-play simulations with Monte Carlo tree search and LLM-based reflection, which can then be used to learn high-level strategic skills such as how to evaluate states that guide the low-level execution.We showcase how our method can be used in both action planning and dialogue generation in the context of games, achieving good performance on both tasks. Specifically, we demonstrate that our method can help train agents with better performance than both traditional reinforcement learning-based approaches and other LLM-based skill learning approaches in games including the Game of Pure Strategy (GOPS) and The Resistance: Avalon.<|reference_end|>
@article{light2024strategist:,
    title={Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search},
    author={Jonathan Light and Min Cai and Weiqin Chen and Guanzhi Wang and Xiusi
  Chen and Wei Cheng and Yisong Yue and Ziniu Hu},
    journal={arXiv preprint arXiv:2408.10635},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10635},
    primaryClass={cs.AI cs.CL}
}