促成政策:利用大型语言模型指导在公共卫生紧急情况下的深度强化学习
Hao Yang1, Chijun Zhang2, Feiyang Yang3
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, Jilin, China.
Computer methods and programs in biomedicine
|February 24, 2026
概括
本研究介绍了一种用于公共卫生紧急情况的混合智能代理,它结合了双深Q网络 (D2QN-JDA) 和大型语言模型 (LLM) 以实现高效的资源配置. 这种新的方法提高了决策准确性,并降低了危机管理的成本.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 在公共卫生紧急情况中,有效的决策对于最佳的资源配置至关重要.
- 传统的优化方法面临着计算复杂性和实时数据集成的挑战.
- 需要先进的智能系统来支持在不确定性下复杂的资源配置.
研究的目的:
- 开发和评估一种混合智能代理,用于在公共卫生紧急情况中加强决策.
- 通过在不确定的条件下平衡多个目标来提高资源分配效率.
- 将大型语言模型与深度强化学习相集成,用于实时数据处理和决策支持.
主要方法:
- 开发了一种混合智能代理,结合了增强的双深Q网络 (D2QN-JDA) 和大型语言模型 (LLM).
- D2QN-JDA包含了联合国家行动输入,动态探索和适应性奖励正常化,以改善学习.
- 检索增强生成 (RAG) 被LLM组件用于整合不同的数据类型.
主要成果:
- 与传统算法 (动态编程,贪,遗传算法,Q学习) 相比,D2QN-JDA表现出优异的性能,从而降低了成本.
- 与手动和regex方法相比,LLM组件显著提高了数据提取精度,回忆,F1得分,成本和时间.
- 香港COVID-19疫情的实验结果验证了该框架的有效性.
结论:
- 拟议的混合智能代理框架有效地解决了公共卫生危机中复杂的,多目标的资源分配挑战.
- D2QN-JDA和LLMs的整合为不确定的环境中实时决策支持提供了强大的解决方案.
- 这种方法提高了管理公共卫生紧急情况的运营效率和成本效益.
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