LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning

In recent years, large language models (LLMs) have shown significant advancements in natural language processing (NLP), with strong capabilities in generation, comprehension, and reasoning. These models have found applications in education, intelligent decision-making, and gaming. However, effectively utilizing LLMs for strategic planning and decision-making in the game of Gomoku remains a challenge. This study aims to develop a Gomoku AI system based on LLMs, simulating the human learning process of playing chess. The system is designed to understand and apply Gomoku strategies and logic to make rational decisions. The research methods include enabling the model to "read the board," "understand the rules," "select strategies," and "evaluate positions," whil e enhancing its abilities through self -play and reinforcement learning. The results demonstrate that this approach significantly improves the selection of move positions, resolves the issue of generating illegal positions, and reduces process time through parallel position evaluation. After extensive self -play training, the model's Gomoku-playing capabilities have been notably enhanced.