 In this paper, we explore the use of shared representations between tasks in order to improve decision-making in non-stationary environments. Our proposed algorithm leverages the fact that the same low-dimensional feature extractor can be used to represent tasks in different environments, allowing us to make decisions more efficiently than existing approaches which treat tasks individually. We prove that our algorithm outperforms other algorithms on both synthetic and real data sets, demonstrating its effectiveness in practice.