To accelerate the pace at which they acquire new information, reinforcement learning algorithms can select which data to use first for training. In this paper, we outline a general methodology to perform this selection process, hinting at a generation of agents which deeply think about their current and future learning state while selecting their training data. In the context of prioritization methods for asynchronous dynamic programming, we propose a meta-level technique for state selection. We show that the method, called meta dynamic programming, together with its approximations, can provide promising performance improvements while being grounded on a theoretically sound metacognitive formalization.