用大模型模拟玩家行为，低成本优化网游机制设计。
Beyond Playtesting: A Generative Multi-Agent Simulation System for Massively Multiplayer Online Games
- 基于真实玩家数据微调大模型，生成拟真玩家决策。
- 模拟系统与真实游戏行为高度一致，干预后反应合理。
- 适合游戏设计师快速验证机制，无需大规模实测。
优化大型多人在线（MMO）游戏的数值系统和机制设计对提升玩家体验至关重要。传统方法依赖大规模线上实验或预设统计模型的参数调优，成本高、耗时长且可能影响玩家体验。虽有简化版离线仿真系统作为替代，但其保真度有限，难以准确模拟真实玩家的推理与反应。为此，我们提出一种基于大语言模型（LLMs）的生成式多智能体模拟系统。通过在大规模真实玩家行为数据上进行监督微调（SFT）与强化学习（RL），将通用大模型适配至游戏领域，实现真实且可解释的玩家决策。同时，基于真实游戏日志训练的数据驱动环境模型重建动态游戏系统。实验表明，该系统与真实玩家行为高度一致，干预后的因果响应合理，提供了一种可靠、可解释且低成本的数据驱动数值设计优化框架。
原文摘要 · Abstract (English)
Optimizing numerical systems and mechanism design is crucial for enhancing player experience in Massively Multiplayer Online (MMO) games. Traditional optimization approaches rely on large-scale online experiments or parameter tuning over predefined statistical models, which are costly, time-consuming, and may disrupt player experience. Although simplified offline simulation systems are often adopted as alternatives, their limited fidelity prevents agents from accurately mimicking real player reasoning and reactions to interventions. To address these limitations, we propose a generative agent-based MMO simulation system empowered by Large Language Models (LLMs). By applying Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on large-scale real player behavioral data, we adapt LLMs from general priors to game-specific domains, enabling realistic and interpretable player decision-making. In parallel, a data-driven environment model trained on real gameplay logs reconstructs dynamic in-game systems. Experiments demonstrate strong consistency with real-world player behaviors and plausible causal responses under interventions, providing a reliable, interpretable, and cost-efficient framework for data-driven numerical design optimization.
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