Imitation learning field has produced learning and inference algorithms that produce regret minimizing policies that don’t depend on the length of the sequence of decisions. Naively training will likely lead to “label bias”. NNs manage to delay it significantly due to very good learned representations of data. But without proof that the learning algorithm produces bounded regret or that the inference/learning is not done over joint loss (of sequence of decisions) the error will most likely accumulate .