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State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, RNNs can be hard to specify and train for non-experts.arXiv:1807.06763v3 fatcat:4sqz4sncfrc4lk6argt46uwfha