用强化学习自动生成适配玩家的战斗，让游戏更刺激又不失公平。
NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons
- 将战斗设计建模为上下文赌博机，实时调整敌人配置。
- 战斗时长提升200%，玩家受伤更深但团灭率低。
- 适合想提升策略性、减少手动设计负担的桌游设计者。
在《龙与地下城》（D&D）中平衡战斗难度是一项复杂任务，需跑团者（DM）手动评估队伍实力、敌方构成及动态玩家互动，同时避免打断叙事节奏。本文提出基于强化学习的遭遇生成方法（NTRL），通过将问题建模为上下文赌博机，根据实时队伍属性自动生成战斗遭遇，实现动态难度调节（DDA）。相比传统跑团规则，NTRL使战斗时长延长200%，对玩家造成的伤害增加，战后平均生命值下降16.67%，并提高玩家死亡数，同时保持总团灭（TPK）率较低。高强度战斗促使玩家采取更谨慎策略与战术操作，即便生成遭遇的胜率高达70%。相较于人类跑团者设计的遭遇，NTRL在提升战斗战略深度的同时，以更公平的方式增强挑战性。
原文摘要 · Abstract (English)
Balancing combat encounters in Dungeons & Dragons (D&D) is a complex task that requires Dungeon Masters (DM) to manually assess party strength, enemy composition, and dynamic player interactions while avoiding interruption of the narrative flow. In this paper, we propose Encounter Generation via Reinforcement Learning (NTRL), a novel approach that automates Dynamic Difficulty Adjustment (DDA) in D&D via combat encounter design. By framing the problem as a contextual bandit, NTRL generates encounters based on real-time party members attributes. In comparison with classic DM heuristics, NTRL iteratively optimizes encounters to extend combat longevity (+200%), increases damage dealt to party members, reducing post-combat hit points (-16.67%), and raises the number of player deaths while maintaining low total party kills (TPK). The intensification of combat forces players to act wisely and engage in tactical maneuvers, even though the generated encounters guarantee high win rates (70%). Even in comparison with encounters designed by human Dungeon Masters, NTRL demonstrates superior performance by enhancing the strategic depth of combat while increasing difficulty in a manner that preserves overall game fairness.
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