Dialogue state tracking (DST) leverages dialogue information to predict dialogues states which are generally represented as slot-value pairs. However, previous work usually has limitations to efficiently predict values due to the lack of a powerful strategy for generating values from both the dialogue history and the predefined values. By predicting values from the predefined value set, previous discriminative DST methods are difficult to handle unknown values. Previous generative DST methods determine values based on mentions in the dialogue history, which makes it difficult for them to handle uncovered and non-pointable mentions. Besides, existing generative DST methods usually ignore the unlabeled instances and suffer from the label noise problem, which limits the generation of mentions and eventually hurts performance. In this paper, we propose a unified shared-private network (USPN) to generate values from both the dialogue history and the predefined values through a unified strategy. Specifically, USPN uses an encoder to construct a complete generative space for each slot and to discern shared information between slots through a shared-private architecture. Then, our model predicts values from the generative space through a shared-private decoder. We further  utilize reinforcement learning to alleviate the label noise problem by learning indirect supervision from semantic relations between conversational words and predefined slot-value pairs. Experimental results on three public datasets show the effectiveness of USPN by outperforming state-of-the-art baselines in both supervised and unsupervised DST tasks.