Since the first wave of the COVID-19 pandemic, governments have applied
restrictions in order to slow down its spreading. However, creating such
policies is hard, especially because the government needs to trade-off the
spreading of the pandemic with the economic losses. For this reason, several
works have applied machine learning techniques, often with the help of
special-purpose simulators, to generate policies that were more effective than
the ones obtained by governments. While the performance of such approaches are
promising, they suffer from a fundamental issue: since such approaches are
based on black-box machine learning, their real-world applicability is limited,
because these policies cannot be analyzed, nor tested, and thus they are not
trustable. In this work, we employ a recently developed hybrid approach, which
combines reinforcement learning with evolutionary computation, for the
generation of interpretable policies for containing the pandemic. These
policies, trained on an existing simulator, aim to reduce the spreading of the
pandemic while minimizing the economic losses. Our results show that our
approach is able to find solutions that are extremely simple, yet very
powerful. In fact, our approach has significantly better performance (in
simulated scenarios) than both previous work and government policies.

本文利用混合强化学习和进化计算的方法，基于现有的模拟器生成可解释的政策，以在减少 COVID-19 传播的同时最小化经济损失，与先前的研究和政府政策相比，得出了具有更好性能的简化的政策。

流行病政策制定的可解释人工智能

Interpretable AI for policy-making in pandemics

Generalized planning is concerned with the characterization and computation
of plans that solve many instances at once. In the standard formulation, a
generalized plan is a mapping from feature or observation histories into
actions, assuming that the instances share a common pool of features and
actions. This assumption, however, excludes the standard relational planning
domains where actions and objects change across instances. In this work, we
extend the standard formulation of generalized planning to such domains. This
is achieved by projecting the actions over the features, resulting in a common
set of abstract actions which can be tested for soundness and completeness, and
which can be used for generating general policies such as "if the gripper is
empty, pick the clear block above x and place it on the table" that achieve the
goal clear(x) in any Blocksworld instance. In this policy, "pick the clear
block above x" is an abstract action that may represent the action Unstack(a,
b) in one situation and the action Unstack(b, c) in another. Transformations
are also introduced for computing such policies by means of fully observable
non-deterministic (FOND) planners. The value of generalized representations for
learning general policies is also discussed.

本文将广义规划的标准公式扩展到包括关系型规划域，通过使用抽象动作进行策略生成和基于 FOND 计划器的计算。