用双层智能体系统让打斗游戏对手更有趣，提升玩家体验。
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
- 分两层设计：底层用强化学习生成多样化高手，上层用大模型根据玩家反馈选对手。
- 高级操作执行率提升64.36%至156.36%，对手风格差异明显。
- 适合游戏设计、体验优化研究者，尤其关注玩家乐趣的团队。
深度强化学习（DRL）已在多种游戏类型中有效提升游戏体验与设计。然而，针对格斗类游戏的智能体研究较少聚焦于提升玩家乐趣这一关键因素。为填补空白并建立以乐趣为导向的智能体设计基准，本文提出双层代理系统（TTA），并在经典格斗游戏《街头霸王II》中开展实验。第一层采用任务导向网络架构、模块化奖励函数与混合训练，生成多样且高技能的DRL代理。第二层引入大语言模型超代理，基于玩家行为数据与反馈动态选择适配的对手。同时，我们建模并分析了影响对手可玩性的多个关键因素。实验显示，先进技能执行率较基线方法提升64.36%至156.36%。训练出的代理展现出显著不同的对战风格。此外，小规模用户研究的反馈表明，整体游玩乐趣显著提升，验证了该系统的有效性。
原文摘要 · Abstract (English)
Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focused explicitly on enhancing player enjoyment, a critical factor for both developers and players. To address this gap and establish a practical baseline for designing enjoyability-focused agents, we propose a two-tier agent (TTA) system and conducted experiments in the classic fighting game Street Fighter II. The first tier of TTA employs a task-oriented network architecture, modularized reward functions, and hybrid training to produce diverse and skilled DRL agents. In the second tier of TTA, a Large Language Model Hyper-Agent, leveraging players' playing data and feedback, dynamically selects suitable DRL opponents. In addition, we investigate and model several key factors that affect the enjoyability of the opponent. The experiments demonstrate improvements from 64. 36% to 156. 36% in the execution of advanced skills over baseline methods. The trained agents also exhibit distinct game-playing styles. Additionally, we conducted a small-scale user study, and the overall enjoyment in the player's feedback validates the effectiveness of our TTA system.
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