Large Language Models as Pokémon Battle Agents: Strategic Play and Content Generation
This work addresses the problem of creating adaptive game AI for interactive entertainment, though it is incremental in applying existing LLMs to a new domain.
The researchers investigated whether large language models (LLMs) could function as competent Pokémon battle agents, testing their ability to make strategic decisions and generate balanced game content. Results showed LLMs could serve as dynamic opponents without domain-specific training, offering a practical alternative to reinforcement learning for turn-based games.
Strategic decision-making in Pokémon battles presents a unique testbed for evaluating large language models. Pokémon battles demand reasoning about type matchups, statistical trade-offs, and risk assessment, skills that mirror human strategic thinking. This work examines whether Large Language Models (LLMs) can serve as competent battle agents, capable of both making tactically sound decisions and generating novel, balanced game content. We developed a turn-based Pokémon battle system where LLMs select moves based on battle state rather than pre-programmed logic. The framework captures essential Pokémon mechanics: type effectiveness multipliers, stat-based damage calculations, and multi-Pokémon team management. Through systematic evaluation across multiple model architectures we measured win rates, decision latency, type-alignment accuracy, and token efficiency. These results suggest LLMs can function as dynamic game opponents without domain-specific training, offering a practical alternative to reinforcement learning for turn-based strategic games. The dual capability of tactical reasoning and content creation, positions LLMs as both players and designers, with implications for procedural generation and adaptive difficulty systems in interactive entertainment.