让智能体学会总结成功经验，提升文本游戏中的决策能力。
Positive Experience Reflection for Agents in Interactive Text Environments
- 引入正向经验记忆机制，增强决策上下文
- 在小模型上仍显著提升性能，优于传统反思方法
- 适合需要持续学习的交互式文本任务场景
面向文本交互环境的智能体面临复杂推理与适应性挑战。基于大语言模型（LLM）的自反思方法虽有潜力，但在初始表现良好时效果下降，且在小模型上表现不佳。本文提出Sweet&Sour方法，通过融合正向经验与可控记忆机制，丰富智能体决策时的上下文信息。实验覆盖闭源与开源多类LLM，结果表明该方法在以往方法表现薄弱的场景中显著提升代理性能。
原文摘要 · Abstract (English)
Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents based on large language models (LLMs) using self-reflection have shown promise, they struggle when initially successful and exhibit reduced effectiveness when using smaller LLMs. We introduce Sweet&Sour, a novel approach that addresses these limitations in existing reflection methods by incorporating positive experiences and managed memory to enrich the context available to the agent at decision time. Our comprehensive analysis spans both closed- and open-source LLMs and demonstrates the effectiveness of Sweet&Sour in improving agent performance, particularly in scenarios where previous approaches fall short.
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