Neutron
reflectometry has long been a powerful tool to study the
interfacial properties of energy materials. Recently, time-resolved
neutron reflectometry has been used to better understand transient
phenomena in electrochemical systems. Those measurements often comprise
a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information
content compared to a typical steady-state measurement. In this work,
we present an approach that leverages existing reinforcement learning
tools to model time-resolved data to extract the time evolution of
structure parameters. By mapping the reflectivity curves taken at
different times as individual states, we use the Soft Actor-Critic
algorithm to optimize the time series of structure parameters that
best represent the evolution of an electrochemical system. We show
that this approach constitutes an elegant solution to the modeling
of time-resolved neutron reflectometry data.