让智能体学会多尺度提炼有用经验，提升长期决策能力
MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making
- 通过三阶段流程提取不同层级的有用经验
- 在多个任务上比基线策略提升规划效果
- 适合需要长期记忆与适应性的智能体研究
长期记忆对智能体至关重要，其中洞察力发挥关键作用。然而，无关洞察的出现和通用洞察的缺乏会显著削弱洞察的有效性。为此，本文提出多尺度洞察智能体（MSI-Agent），一种通过跨尺度有效总结与利用洞察来增强大语言模型规划与决策能力的具身智能体。MSI采用经验选择器、洞察生成器和洞察选择器构成的三阶段流程，能够生成任务特定且高层次的洞察，将其存入数据库，并在决策时调用相关洞察。实验表明，相较于其他洞察策略，MSI在使用GPT3.5进行规划时表现更优。此外，我们深入探讨了种子经验与洞察的选择策略，旨在为大语言模型提供更实用、更相关的洞察以提升决策质量。观察还显示，当面对领域迁移场景时，MSI表现出更强的鲁棒性。
原文摘要 · Abstract (English)
Long-term memory is significant for agents, in which insights play a crucial role. However, the emergence of irrelevant insight and the lack of general insight can greatly undermine the effectiveness of insight. To solve this problem, in this paper, we introduce Multi-Scale Insight Agent (MSI-Agent), an embodied agent designed to improve LLMs' planning and decision-making ability by summarizing and utilizing insight effectively across different scales. MSI achieves this through the experience selector, insight generator, and insight selector. Leveraging a three-part pipeline, MSI can generate task-specific and high-level insight, store it in a database, and then use relevant insight from it to aid in decision-making. Our experiments show that MSI outperforms another insight strategy when planning by GPT3.5. Moreover, We delve into the strategies for selecting seed experience and insight, aiming to provide LLM with more useful and relevant insight for better decision-making. Our observations also indicate that MSI exhibits better robustness when facing domain-shifting scenarios.
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