Infectious disease outbreaks can have a disruptive impact on public health
and societal processes. As decision making in the context of epidemic
mitigation is hard, reinforcement learning provides a methodology to
automatically learn prevention strategies in combination with complex epidemic
models. Current research focuses on optimizing policies w.r.t. a single
objective, such as the pathogen's attack rate. However, as the mitigation of
epidemics involves distinct, and possibly conflicting criteria (i.a.,
prevalence, mortality, morbidity, cost), a multi-objective approach is
warranted to learn balanced policies. To lift this decision-making process to
real-world epidemic models, we apply deep multi-objective reinforcement
learning and build upon a state-of-the-art algorithm, Pareto Conditioned
Networks (PCN), to learn a set of solutions that approximates the Pareto front
of the decision problem. We consider the first wave of the Belgian COVID-19
epidemic, which was mitigated by a lockdown, and study different deconfinement
strategies, aiming to minimize both COVID-19 cases (i.e., infections and
hospitalizations) and the societal burden that is induced by the applied
mitigation measures. We contribute a multi-objective Markov decision process
that encapsulates the stochastic compartment model that was used to inform
policy makers during the COVID-19 epidemic. As these social mitigation measures
are implemented in a continuous action space that modulates the contact matrix
of the age-structured epidemic model, we extend PCN to this setting. We
evaluate the solution returned by PCN, and observe that it correctly learns to
reduce the social burden whenever the hospitalization rates are sufficiently
low. In this work, we thus show that multi-objective reinforcement learning is
attainable in complex epidemiological models and provides essential insights to
balance complex mitigation policies.

本研究使用深度多目标强化学习方法，以 Pareto Conditioned Networks (PCN) 算法为基础，尝试学习并平衡 COVID-19 流行病的多种防控策略，涉及病例、医院感染率、社会负担等多个指标，为疫情决策提供了重要洞见。

利用强化学习探究多目标 COVID-19 缓解政策的帕累托前沿

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

Deep convolutional neural networks demonstrate impressive results in the
super-resolution domain. A series of studies concentrate on improving peak
signal noise ratio (PSNR) by using much deeper layers, which are not friendly
to constrained resources. Pursuing a trade-off between the restoration capacity
and the simplicity of models is still non-trivial. Recent contributions are
struggling to manually maximize this balance, while our work achieves the same
goal automatically with neural architecture search. Specifically, we handle
super-resolution with a multi-objective approach. We also propose an elastic
search tactic at both micro and macro level, based on a hybrid controller that
profits from evolutionary computation and reinforcement learning. Quantitative
experiments help us to draw a conclusion that our generated models dominate
most of the state-of-the-art methods with respect to the individual FLOPS.

使用神经架构搜索的多目标方法能够在以 FLOPS 为指标的量化实验中优于现有方法，以在资源受限条件下平衡模型的简单度和修复能力。