R2D2：基于反思式智能体记忆的记住、回放与动态决策
R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory
AI总结：
本文提出R2D2框架，通过记住与反思两种范式增强网络智能体的导航和决策能力，在WebArena基准上实现导航错误减少50%、任务完成率提高三倍。
摘要：
网络智能体的激增要求在复杂网络环境中具备先进的导航与交互策略。现有模型由于对网络结构的可见性和理解有限，往往在高效导航和动作执行方面存在困难。我们提出的R2D2框架通过整合两种范式来应对这些挑战：记住（Remember）和反思（Reflect）。记住范式使用回放缓冲区帮助智能体动态重建网络环境，从而能够构建先前访问页面的详细“地图”。这有助于减少导航错误并优化网络交互过程中的决策过程。相反，反思范式通过提供错误分析和策略优化的机制，使智能体能够从过去的错误中学习，从而提升整体任务性能。我们使用WebArena基准对R2D2进行评估，结果表明其相比现有方法有显著改进，包括导航错误减少50%以及任务完成率提高三倍。我们的研究结果表明，记忆增强导航与反思式学习相结合有望提升网络智能体的能力，并可能有益于自动化客户服务和个人数字助理等多种应用。
英文摘要：
The proliferation of web agents necessitates advanced navigation and interaction strategies within complex web environments. Current models often struggle with efficient navigation and action execution due to limited visibility and understanding of web structures. Our proposed R2D2 framework addresses these challenges by integrating two paradigms: Remember and Reflect. The Remember paradigm uses a replay buffer that aids agents in reconstructing the web environment dynamically, thus enabling the formulation of a detailed "map" of previously visited pages. This helps in reducing navigational errors and optimizing the decision-making process during web interactions. Conversely, the Reflect paradigm allows agents to learn from past mistakes by providing a mechanism for error analysis and strategy refinement, enhancing overall task performance. We evaluate R2D2 using the WebArena benchmark, demonstrating substantial improvements over existing methods, including a 50% reduction in navigation errors and a threefold increase in task completion rates. Our findings suggest that a combination of memory-enhanced navigation and reflective learning promisingly advances the capabilities of web agents, potentially benefiting various applications such as automated customer service and personal digital assistants.
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。
