论文标题
强化学习以优化共价-19缓解政策
Reinforcement Learning for Optimization of COVID-19 Mitigation policies
论文作者
论文摘要
2020年，Covid-19的病毒导致了历史上最糟糕的全球大流行之一。结果，世界各地的政府面临着保护公共卫生的挑战，同时使经济尽可能地持续下去。流行病学模型提供了对这些类型疾病传播的见解，并预测了可能的干预政策的影响。但是，迄今为止，甚至最数据驱动的干预政策都取决于启发式方法。在本文中，我们研究了如何使用强化学习（RL）来优化缓解政策，从而在不压倒医院能力的情况下最小化经济影响。我们的主要贡献是（1）一种基于代理的新型大流行模拟器，与传统模型不同，它能够对社区特定位置的人们之间的细粒度相互作用进行建模； （2）基于RL的方法，用于优化该模拟器中的细粒缓解策略。我们的结果证明了在现实条件下的总体模拟器行为和学习的政策。
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date,the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.
