  In this paper, we study representation learning for multi-task
decision-making in non-stationary environments. We consider the framework of
sequential linear bandits, where the agent performs a series of tasks drawn
from distinct sets associated with different environments. The embeddings of
tasks in each set share a low-dimensional feature extractor called
representation, and representations are different across sets. We propose an
online algorithm that facilitates efficient decision-making by learning and
transferring non-stationary representations in an adaptive fashion. We prove
that our algorithm significantly outperforms the existing ones that treat tasks
independently. We also conduct experiments using both synthetic and real data
to validate our theoretical insights and demonstrate the efficacy of our
algorithm.
