Our work is connected to active learning, for example, to approaches that use reinforcement learning to learn a policy for a dynamic active learning strategy (Fang et al., 2017), or to learn a curriculum to order noisy examples (Kumar et al., 2019), or to the approach of  who use imitation learning to select batches of data to be labeled.
the action space these approaches consider is restricted to the decision whether or not to select particular data and is designed for a fixed budget, neither do they incorporate feedback cost in their frameworks.