自然语言强化学习：一个可处理语言反馈的强化学习框架

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在人工智能发展史上，强化学习(RL)凭借其严谨的数学框架解决了众多复杂的决策问题，从围棋、国际象棋到机器人控制等领域都取得了突破性进展。然而，随着应用场景日益复杂，传统强化学习过度依赖单一数值奖励的局限性日益凸显。在现实世界中，反馈信号往往是多维度、多模态的，例如教练的口头指导、视觉示范，或是详细的文字说明。近日，来自伦敦大学学院、上海交通大学、布朗大学、新加坡国立大学和布里斯托大学的联合研究团队提出了全新的自然语言强化学习（NaturalLanguageReinforcementLearning,NLRL）范式，成功将强化学习的核心概念类比为基于自然语言的形式，开辟了一条通向更智能、更自然的AI决策学习的新道路。

从数值到语言：新范式的萌芽

自然语言强化学习

从理论到实践

具体来说，当系统需要评估某个状态时，它会：

1.从该状态开始采样K条完整轨迹

2.将每条轨迹转化为详细的文本描述

3.使用专门设计的提示让LLM扮演“专家评估员”的角色

4.LLM分析所有轨迹描述，提取关键模式和见解

在NLRL中，语言时序差分学习包含三个关键组件：

1.文本描述生成器d：将状态转换(s,a,r,s')转化为自然语言描述

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自然语言强化学习：一个可处理语言反馈的强化学习框架

自然语言强化学习：一个可处理语言反馈的强化学习框架

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