用知识图谱增强大模型任务规划，提升泛化与扩展能力
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning
- 构建指令图存储历史正确操作路径，结合强化学习扩展覆盖
- 多智能体框架下实现19.2%性能提升，新任务适应更快
- 适合需要高效规划和跨任务迁移的AI代理研究者
大语言模型在复杂任务规划中展现潜力，但受限于自身知识。现有方法依赖思维-行动-观察流程，难以应对复杂任务。本文提出InstructRAG，基于多智能体元强化学习框架，解决可扩展性与可迁移性两大挑战。该方法构建指令图组织过往正确操作序列，通过强化学习智能体扩展图覆盖范围以增强可扩展性，通过元学习智能体提升任务泛化能力以改善可迁移性。两个智能体端到端联合训练，优化整体规划性能。在四个主流任务规划数据集上的实验表明，InstructRAG显著提升性能，相较最优现有方法最高提升19.2%，且能高效适应新任务。
原文摘要 · Abstract (English)
Recent advancements in large language models (LLMs) have enabled their use as agents for planning complex tasks. Existing methods typically rely on a thought-action-observation (TAO) process to enhance LLM performance, but these approaches are often constrained by the LLMs' limited knowledge of complex tasks. Retrieval-augmented generation (RAG) offers new opportunities by leveraging external databases to ground generation in retrieved information. In this paper, we identify two key challenges (enlargability and transferability) in applying RAG to task planning. We propose InstructRAG, a novel solution within a multi-agent meta-reinforcement learning framework, to address these challenges. InstructRAG includes a graph to organize past instruction paths (sequences of correct actions), an RL-Agent with Reinforcement Learning to expand graph coverage for enlargability, and an ML-Agent with Meta-Learning to improve task generalization for transferability. The two agents are trained end-to-end to optimize overall planning performance. Our experiments on four widely used task planning datasets demonstrate that InstructRAG significantly enhances performance and adapts efficiently to new tasks, achieving up to a 19.2% improvement over the best existing approach.
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