This paper presents the submissions by the University of Zurich to the CoNLL–SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection. Our system is based on the prior work on neural transitionbased transduction (Makarov and Clematide, 2018b; Aharoni and Goldberg, 2017). Unlike the prior work, we train the model in a fully end-to-end fashion—without the need for an external character aligner—within the framework of imitation learning. In the type-level morphological inflection generation challenge (Task I), our five-strong ensemble outperforms all competitors in all three data-size settings. In the token-level inflection generation challenge (Task II), our single model achieves the best results on three out of four sub-tasks that we have participated in.