However, this causes error propagation problems, since each state depends on the history of the past decisions.
at the k-th iteration and the t-th position of the input sequence, we simply use the predictions of the previously learned classifiers to generate training examples, i.e., Although it is not theoretically justified, it empirically runs as a stochastic version of DAGGER (Ross et al., 2011) or SEARN (Daumé III et al., 2009) with the speed benefit of online learning.