  Earthquakes are lethal and costly. This study aims at avoiding these
catastrophic events by the application of injection policies retrieved through
reinforcement learning. With the rapid growth of artificial intelligence,
prediction-control problems are all the more tackled by function approximation
models that learn how to control a specific task, even for systems with
unmodeled/unknown dynamics and important uncertainties. Here, we show for the
first time the possibility of controlling earthquake-like instabilities using
state-of-the-art deep reinforcement learning techniques. The controller is
trained using a reduced model of the physical system, i.e, the spring-slider
model, which embodies the main dynamics of the physical problem for a given
earthquake magnitude. Its robustness to unmodeled dynamics is explored through
a parametric study. Our study is a first step towards minimizing seismicity in
industrial projects (geothermal energy, hydrocarbons production, CO2
sequestration) while, in a second step for inspiring techniques for natural
earthquakes control and prevention.
