Applying Policy Iteration for Training Recurrent Neural Networks
<|reference_start|>Applying Policy Iteration for Training Recurrent Neural Networks: 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.<|reference_end|>
@article{szita2004applying,
    title={Applying Policy Iteration for Training Recurrent Neural Networks},
    author={I. Szita and A. Lorincz},
    journal={arXiv preprint arXiv:cs/0410004},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0410004},
    primaryClass={cs.AI cs.LG cs.NE}
}