We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. The method decomposes game-solving into four incremental tasks -- game summarization, area selection, action extraction, and action validation -- each assigned to a specific language-model agent. By constructing a tree of thoughts, the method simulates reasoning paths and allows agents to collaboratively distill game representations and tactics, mitigating the limitations of language models in reasoning and long-term memorization. Additionally, an automated fine-tuning process further optimizes the agents' performance by ranking query-response pairs based on game outcomes, e.g., winning or losing. We apply the method to a non-cooperative game and demonstrate a 65 percent winning rate against benchmark algorithms, with an additional 10 percent improvement after fine-tuning. In contrast to existing deep learning algorithms for game solving that require millions of training samples, the proposed method consumes approximately 1000 training samples, highlighting its efficiency and scalability.

本研究解决了预训练语言模型在处理复杂不熟悉游戏时的局限性，提出了一种将思维树与多智能体框架相结合的方法。这种方法分解游戏解决过程为四个增量任务，并应用于对抗性游戏，展示了65%的胜率，相较于基准算法在微调后再提升了10%，强调了其高效性与可扩展性。

对抗性游戏中推理、记忆和微调语言模型的方法