基于深度强化学习的复杂网络中减轻流行病的传播
Jie Yang1, Wenshuang Liu1, Xi Zhang1
1School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Chaos (Woodbury, N.Y.)
|December 19, 2024
概括
本研究使用深度强化学习 (DRL) 来确定复杂网络中的最佳隔离目标,平衡流行病控制与经济成本. 该DRL战略有效地减轻了传染病的传播,在关键的隔离规模之外的回报率下降.
科学领域:
- 网络科学 网络科学
- 流行病学 流行病学
- 计算科学 计算科学
背景情况:
- 复杂的网络容易受到传染级联的影响,需要有效的流行病缓解.
- 物理隔离是有效的,但如果不战略性地实施,可能会产生重大经济成本.
研究的目的:
- 开发一种创新的,具有成本效益的战略,用于选择复杂网络中的隔离目标.
- 尽量减少流行病的传播和与隔离有关的经济影响.
主要方法:
- 使用马尔科夫链与随机过渡和节点隔离进行流行病传播的建模.
- 使用深度强化学习 (DRL),特别是近接政策优化算法,来训练隔离策略.
- 在合成和现实世界的网络数据集上进行模拟.
主要成果:
- 基于DRL的隔离策略有效控制了复杂网络中的流行病传播.
- 在每日最大检疫规模和缓解效果之间观察到非线性关系,显示在临界值后回报率下降.
- 该战略成功地平衡了感染率的降低和隔离成本.
结论:
- 深度强化学习为优化复杂网络中的流行病缓解策略提供了一种强大的方法.
- 了解隔离规模的非线性影响对于有效的资源配置和政策制定至关重要.
- 拟议的方法为经济上可行的流行病应对计划提供了一个框架.
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