  Recurrent neural networks are often used for learning time-series data. Based
on a few assumptions we model this learning task as a minimization problem of a
nonlinear least-squares cost function. The special structure of the cost
function allows us to build a connection to reinforcement learning. We exploit
this connection and derive a convergent, policy iteration-based algorithm.
Furthermore, we argue that RNN training can be fit naturally into the
reinforcement learning framework.
