MSI-Agent：將多尺度洞察力融入具體代理人，以提升規劃和決策能力

MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making

September 25, 2024

作者: Dayuan Fu, Biqing Qi, Yihuai Gao, Che Jiang, Guanting Dong, Bowen Zhou

cs.AI

摘要

對於代理人而言，長期記憶至關重要，其中洞察扮演著關鍵角色。然而，不相關的洞察出現以及缺乏一般性洞察可能會嚴重削弱洞察的效力。為了解決這個問題，在本文中，我們介紹了多尺度洞察代理人（MSI-Agent），這是一個具體化代理人，旨在通過有效地概括和利用不同尺度上的洞察來提高LLMs的規劃和決策能力。MSI通過經驗選擇器、洞察生成器和洞察選擇器實現這一目標。利用三部分流程，MSI能夠生成任務特定和高層次的洞察，將其存儲在數據庫中，然後利用其中的相關洞察來幫助決策。我們的實驗表明，當使用GPT3.5進行規劃時，MSI在超越另一種洞察策略方面表現出色。此外，我們深入探討了選擇種子經驗和洞察的策略，旨在為LLM提供更有用和相關的洞察，以便做出更好的決策。我們的觀察還表明，當面臨領域轉移情況時，MSI表現出更好的穩健性。

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.