Optimization of Decision Making Capabilities of Intelligent Characters in Strategy Games Based on Large Language Model Agent

In order to explore the optimization of decision-making ability of intelligent characters in strategy games based on Large Language Model (LLM) Agent, this paper takes pokellmon[1] developed by Georgia Institute of Technology as an example, and realizes the exploration of the scheme of optimization of decision-making ability by imitating the human players: 1. Collecting the experience of various human players and letting the LLM to expand the knowledge by means of Prompt Engineering and RAG(Retrieval-Augmented Generation) to get that experience; 2. Collecting the records of human high-scoring matchups records from the The replays records on pokemon showdown official website, and make a dataset of multiple rounds records of each game in the mode of multi-round dialogues, and fine-tune the LLM through QLoRA fine-tuning method (Tim Dettmers et al., 2023) after processing the data, so as to allow the LLM to learn the decision-making of the human masters in the random battles. The open-source LLM llamma3.1-8b was chosen for this study, and the battles against the heuristic bots showed that the modified pokellmon got some degree of strategy optimization, and compared to the very beginning of the battles against the heuristic bots, the winning rate was improved from 18% to 34%.