Conventional tile-based 360° video streaming methods, including deep reinforcement learning (DRL) based, ignore the interactive nature of 360° video streaming and download tiles following fixed sequential orders, thus failing to respond to the user's head motion changes. We show that these existing solutions suffer from either the prefetch accuracy or the playback stability drop. Furthermore, these methods are constrained to serve only one fixed streaming preference, causing extra training overhead and the lack of generalization on unseen preferences. In this paper, we propose a dual-queue streaming framework, with accuracy and stability purposes respectively, to enable the DRL agent to determine and change the tile download order without incurring overhead. We also design a preference-aware DRL algorithm to incentivize the agent to learn preference-dependent ABR decisions efficiently. Compared with state-of-the-art DRL baselines, our method not only significantly improves the streaming quality, e.g., increasing the average streaming quality by 13.6% on a public dataset, but also demonstrates better performance and generalization under dynamic preferences, e.g., an average quality improvement of 19.9% on unseen preferences.