Can LLMs power strong game AI agents?
PokéChamp: an Expert-level Minimax Language Agent
This paper introduces PokéChamp, an AI agent that plays Pokémon at an expert level by combining Large Language Models (LLMs) with the minimax tree search algorithm. The LLM enhances the search by suggesting player actions, predicting opponent actions, and estimating the value of game states, effectively incorporating strategic knowledge and handling the game's partial observability. Notably for multi-agent systems development, PokéChamp's framework uses the LLM as a black box, requires no LLM fine-tuning, and is generally applicable to two-player zero-sum games. Evaluation shows strong performance against other bots, including another LLM-based agent, and competitive performance against human players online. The research also contributes a large Pokémon battle dataset and benchmarks for evaluating agent performance.