通过技能图谱实现游戏中的持续学习，仅需微调少数技能即可适应新环境。
Learning Transferable Skills in Action RPGs via Directed Skill Graphs and Selective Adaptation
- 将战斗拆解为五种可复用技能，按层级课程训练
- 仅微调两个技能，即在有限交互下快速恢复性能
- 适合长期学习、动态环境下的智能体开发
终身智能体应在不从头训练或覆盖旧行为的前提下持续扩展能力。我们在《黑暗之魂3》这一复杂实时控制场景中研究此问题，将战斗建模为有向技能图，并采用分层课程训练其组件。所得智能体将控制分解为五种可重用技能：摄像机控制、目标锁定、移动、闪避以及治疗-攻击决策策略，每项均针对特定职责优化。这种分解提升了样本效率，减轻单个策略负担，并支持选择性后期适应：当环境从第1阶段切换至第2阶段时，仅需调整部分技能，上游技能仍可迁移。实验表明，在有限交互预算下，仅对两个技能进行针对性微调即可迅速恢复性能，表明技能图谱与选择性微调共同为复杂实时环境中持续学习智能体提供了可行路径。
原文摘要 · Abstract (English)
Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this in a challenging real-time control setting (Dark Souls III) by representing combat as a directed skill graph and training its components in a hierarchical curriculum. The resulting agent decomposes control into five reusable skills: camera control, target lock-on, movement, dodging, and a heal-attack decision policy, each optimized for a narrow responsibility. This factorization improves sample efficiency by reducing the burden on any single policy and supports selective post-training: when the environment shifts from Phase 1 to Phase 2, only a subset of skills must be adapted, while upstream skills remain transferable. Empirically, we find that targeted fine-tuning of just two skills rapidly recovers performance under a limited interaction budget, suggesting that skill-graph curricula together with selective fine-tuning offer a practical pathway toward evolving, continually learning agents in complex real-time environments.
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