论文标题
使用多代理增强学习的微观流行模型和大流行预测
A Microscopic Epidemic Model and Pandemic Prediction Using Multi-Agent Reinforcement Learning
论文作者
论文摘要
本文介绍了一种模拟流行病的微观方法，该方法可以明确地考虑个人决定对疾病传播的后果。我们首先制定了一个微观的多学院流行模型，每个药物都可以选择影响疾病传播的活性水平。然后，通过最大程度地减少代理的成本功能，我们在游戏理论和多代理增强学习框架中为单个代理的最佳决策做出了解决。鉴于所有药物的最佳决定，我们可以对疾病的传播做出预测。我们表明，有负面的外部性是因为感染者没有足够的动力来保护他人，这需要外部干预措施来调节药物的行为。在讨论部分中，指出未来的方向使模型更加现实。
This paper introduces a microscopic approach to model epidemics, which can explicitly consider the consequences of individual's decisions on the spread of the disease. We first formulate a microscopic multi-agent epidemic model where every agent can choose its activity level that affects the spread of the disease. Then by minimizing agents' cost functions, we solve for the optimal decisions for individual agents in the framework of game theory and multi-agent reinforcement learning. Given the optimal decisions of all agents, we can make predictions about the spread of the disease. We show that there are negative externalities in the sense that infected agents do not have enough incentives to protect others, which then necessitates external interventions to regulate agents' behaviors. In the discussion section, future directions are pointed out to make the model more realistic.
