用来自大型语言模型的背景知识提高强化学习的样本效率
IEEE transactions on neural networks and learning systems
|August 14, 2025
概括
本研究引入了一种使用大型语言模型 (LLM) 来提取一般环境知识的新框架,显著提高了强化学习 (RL) 任务中的样本效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 强化学习 (RL) 面临的挑战是样本效率低.
- 目前使用大型语言模型 (LLM) 为RL指导的方法缺乏跨任务的通用性.
- 需要可重复使用的环境知识来加速RL政策学习.
研究的目的:
- 开发一个利用LLM来提取环境的可概括的背景知识的框架.
- 将这些知识作为潜在的函数来表示,以便在RL中有效地塑造奖励.
- 通过一次性知识表示,提高各种下游RL任务的样本效率.
主要方法:
- 用事先收集的环境经验为LLM奠定基础.
- 促使LLM使用代码生成,偏好注释和目标赋值等方法来界定背景知识.
- 将提取的知识表示为潜在函数,用于基于潜力的奖励塑造.
主要成果:
- 在多个RL任务中显示出样本效率的显著改善.
- 验证了框架在Minigrid和Crafter领域的有效性.
- 展示了为各种下游任务提取的环境知识的概括性.
结论:
- 拟议的框架有效地利用LLM来捕获和表示一般环境知识.
- 这种方法显著提高了强化学习中的样本效率.
- 该方法为创造更具适应性和效率的RL代理提供了一个有希望的方向.
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