利用强化学习规划多种流行病干预措施
Planning Multiple Epidemic Interventions with Reinforcement Learning
AI总结：
该研究将多种连续流行病干预规划建模为马尔可夫决策过程，并利用 PPO 与 SAC 强化学习算法在常微分方程疾病模型中搜索低成本方案，实验表明其优于手工政策基线。
摘要：
抗击流行病需要找到一项计划，说明何时以及如何实施不同的干预措施，例如强制佩戴口罩、疫苗接种、关闭学校或工作场所。最优计划将以最小的生命损失、疾病负担和经济成本遏制流行病。在现实场景中，寻找最优计划是一个难以处理的计算问题。然而，如果工具能够高效搜索可最小化疾病与经济成本的计划，特别是在连续且同样复杂的状态空间下，于连续且复杂的动作空间中考虑多种可能的干预措施，将极大地造福政策制定者。我们将该问题形式化为马尔可夫决策过程。我们的形式化具有独特能力，可在任何由常微分方程定义的疾病模型上表示多种连续干预措施。我们说明了如何有效应用最先进的 actor-critic 强化学习算法（PPO 和 SAC）来搜索最小化总成本的计划。我们通过实验评估这些算法的学习性能，并将其性能与模拟政策制定者所构建计划的手工基线进行比较。我们的方法优于基线。本工作证实了用计算方法支持政策制定者的可行性。
英文摘要：
Combating an epidemic entails finding a plan that describes when and how to apply different interventions, such as mask-wearing mandates, vaccinations, school or workplace closures. An optimal plan will curb an epidemic with minimal loss of life, disease burden, and economic cost. Finding an optimal plan is an intractable computational problem in realistic settings. Policy-makers, however, would greatly benefit from tools that can efficiently search for plans that minimize disease and economic costs especially when considering multiple possible interventions over a continuous and complex action space given a continuous and equally complex state space. We formulate this problem as a Markov decision process. Our formulation is unique in its ability to represent multiple continuous interventions over any disease model defined by ordinary differential equations. We illustrate how to effectively apply state-of-the-art actor-critic reinforcement learning algorithms (PPO and SAC) to search for plans that minimize overall costs. We empirically evaluate the learning performance of these algorithms and compare their performance to hand-crafted baselines that mimic plans constructed by policy-makers. Our method outperforms baselines. Our work confirms the viability of a computational approach to support policy-makers
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