Large Language Models as Pok\'emon Battle Agents: Strategic Play and Content Generation

TL;DR

This paper explores the use of large language models as strategic agents and content creators in Pokémon battles, demonstrating their ability to make tactical decisions and generate game content without domain-specific training.

Contribution

It introduces a framework where LLMs act as Pokémon battle agents, capable of strategic play and content generation, highlighting their potential as versatile tools in game design and AI research.

Findings

LLMs achieve competitive win rates in Pokémon battles.

Models demonstrate understanding of type matchups and damage calculations.

LLMs can generate balanced new Pokémon content.

Abstract

Strategic decision-making in Pok\'emon battles presents a unique testbed for evaluating large language models. Pok\'emon 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 Pok\'emon battle system where LLMs select moves based on battle state rather than pre-programmed logic. The framework captures essential Pok\'emon mechanics: type effectiveness multipliers, stat-based damage calculations, and multi-Pok\'emon 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.