SkillGraph：通过演化的技能图增强代理的强化学习
SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs
AI总结：
SkillGraph通过演化的技能图增强代理的强化学习，解决技能组合任务中的依赖识别和库维护问题，实验显示其在复杂任务中表现优异。
摘要：
技能库使大语言模型代理能够重用过去交互的经验，但现有库通常将技能作为孤立条目存储，并仅通过语义相似性检索。这导致了两个关键挑战：首先，代理必须识别相关技能及其相互依赖关系；其次，库维护困难，因系统缺乏决定何时合并、拆分或删除技能的结构线索。我们提出SKILLGRAPH，该框架将可重用技能表示为有向图的节点，有类型边编码先决条件、增强和共现关系。给定新任务，SKILLGRAPH不仅检索个体技能，还检索有序的技能子图，可引导多步决策。图从代理轨迹和强化学习反馈持续更新，使技能库和代理策略共同改进。在ALFWorld、WebShop和七个增强搜索的问答任务上的实验表明，SKILLGRAPH在内存增强RL方法中取得最佳性能，尤其在需要组合多个技能的复杂任务中收益显著。
英文摘要：
Skill libraries enable large language model agents to reuse experience from past interactions, but most existing libraries store skills as isolated entries and retrieve them only by semantic similarity. This leads to two key challenges for compositional tasks. Firstly, an agent must identify not only relevant skills but also how they depend on and build upon each other. Secondly, it also makes library maintenance difficult, since the system lacks structural cues for deciding when skills should be merged, split, or removed. We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph, with typed edges encoding prerequisite, enhancement, and co-occurrence relations. Given a new task, SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making. The graph is continuously updated from agent trajectories and reinforcement learning feedback, allowing both the skill library and the agent policy to improve together. Experiments on ALFWorld, WebShop, and seven search-augmented QA tasks show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods, with especially large gains on complex tasks that require composing multiple skills.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
