  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.
