FSM과 강화학습 기반의 지능형 NPC AI: 2D 모바일 자동 진행 RPG 게임의 구현 연구

초록

This study focuses on the design and implementation of an intelligent NPC AI system that integrates Finite State Machines (FSM) andreinforcement learning in mobile idle RPG games. By combining FSM with reinforcement learning techniques such as Q-learning andDeep Q-Networks (DQN), the system optimizes NPCs’ environmental perception, combat strategies, and state management. The NPC AIdynamically transitions between various states such as idle, attack, and retreat, and experimental evaluation involved collecting andanalyzing performance metrics such as combat win rates, response times, and movement distances to refine behavioral patterns andmaintain game balance. This study proposes a method for enhancing NPC AI performance in idle RPGs through the integrated applicationof FSM and reinforcement learning. Through real-time data analysis, the difficulty and behavior patterns of NPCs were dynamically adjusted,and an intelligent NPC system was implemented that adapts to various combat environments and situations. This research provides aguide for developing auto-roguelike RPG games and can be used as foundational material for enhancing game experiences based onreal-time AI optimization.

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