  3D spot beamfocusing (SBF), in contrast to conventional angular-domain
beamforming, concentrates radiating power within very small volume in both
radial and angular domains in the near-field zone. Recently the implementation
of channel-state-information (CSI)-independent machine learning (ML)-based
approaches have been developed for effective SBF using
extremely-largescale-programable-metasurface (ELPMs). These methods involve
dividing the ELPMs into subarrays and independently training them with Deep
Reinforcement Learning to jointly focus the beam at the Desired Focal Point
(DFP). This paper explores near-field SBF using ELPMs, addressing challenges
associated with lengthy training times resulting from independent training of
subarrays. To achieve a faster CSIindependent solution, inspired by the
correlation between the beamfocusing matrices of the subarrays, we leverage
transfer learning techniques. First, we introduce a novel similarity criterion
based on the Phase Distribution Image of subarray apertures. Then we devise a
subarray policy propagation scheme that transfers the knowledge from trained to
untrained subarrays. We further enhance learning by introducing
Quasi-Liquid-Layers as a revised version of the adaptive policy reuse
technique. We show through simulations that the proposed scheme improves the
training speed about 5 times. Furthermore, for dynamic DFP management, we
devised a DFP policy blending process, which augments the convergence rate up
to 8-fold.
