  The integration of reconfigurable intelligent surfaces (RIS) in wireless
environments offers channel programmability and dynamic control over
propagation channels, which is expected to play a crucial role in sixth
generation (6G) networks. The majority of RIS-related research has focused on
simpler, quasi-free-space conditions, where wireless channels are typically
modeled analytically. However, many practical localization scenarios unfold in
environments characterized by rich scattering that also change over time. These
dynamic and complex conditions pose significant challenges in determining the
optimal RIS configuration to maximize localization accuracy. In this paper, we
present our approach to overcoming this challenge. This paper introduces a
novel approach that leverages a bidirectional long-short term memory (biLSTM)
network, trained with a simulator that accurately reflects wave physics, to
capture the relationship between wireless channels and the RIS configuration
under dynamic, rich-scattering conditions. We use this approach to optimize RIS
configurations for enhanced user equipment (UE) localization, measured by mean
squared error (MSE). Through extensive simulations, we demonstrate that our
approach adapts RIS configurations to significantly improve localization
accuracy in such dynamically changing rich scattering environments.
