  We propose a novel control approach that combines offline supervised learning
to address the challenges posed by non-linear phase reconstruction using
unmodulated pyramid wavefront sensors (P-WFS) and online reinforcement learning
for predictive control. The control approach uses a high-order P-WFS to drive a
tip-tilt stage and a high-dimensional mirror concurrently. Simulation results
demonstrate that our method outperforms traditional control techniques, showing
significant improvements in performance under challenging conditions such as
faint stars and poor seeing, and exhibits robustness against variations in
atmospheric conditions.
