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Even though most interfaces in the real world are discrete, no efficient wayexists to train neural networks to make use of them, yet. We enhance anInteraction Network (a Reinforcement Learning architecture) with discreteinterfaces and train it on the generalized Dyck language. This task requires anunderstanding of hierarchical structures to solve, and has long provendifficult for neural networks. We provide the first solution based on learningto use discrete data structures. We encountered unexpected anomalous behaviorduring training, and utilized pre-training based on execution traces toovercome them. The resulting model is very small and fast, and generalizes tosequences that are an entire order of magnitude longer than the training data.

Enhancing Reinforcement Learning with discrete interfaces to learn the Dyck Language