演化程式化技能網絡（注：此處採用"程式化"對應"programmatic"，強調基於程式碼架構的技能組合；"網絡"對應"networks"，指代相互關聯的技能體系。標題整體體現通過演化計算方法優化程式設計技能結構的技術路徑。）

摘要

我們研究開放式具身環境中的持續技能習得問題，該場景要求智能體構建、優化並複用不斷擴展的可執行技能庫。我們提出程序化技能網絡（PSN），該框架將技能定義為可執行的符號化程序，形成一個可通過經驗演進的組合式網絡。PSN通過大型語言模型實例化三大核心機制：（1）用於技能組合結構化故障定位的REFLECT模塊；（2）具備成熟度感知更新門控的漸進式優化機制，在穩定可靠技能的同時保持對不確定技能的可塑性；（3）基於回滾驗證的規範化重構策略，維持網絡簡潔性。我們進一步揭示PSN的學習動力學與神經網絡訓練存在結構相似性。在MineDojo和Crafter環境中的實驗表明，該方法能實現魯棒的技能複用、快速適應能力，並在開放式任務分佈上展現出強泛化性能。\footnote{我們計劃開源代碼。}

English

We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\footnote{We plan to open-source the code.

演化程式化技能網絡（注：此處採用"程式化"對應"programmatic"，強調基於程式碼架構的技能組合；"網絡"對應"networks"，指代相互關聯的技能體系。標題整體體現通過演化計算方法優化程式設計技能結構的技術路徑。）

Evolving Programmatic Skill Networks

摘要

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