Applying Policy Iteration for Training Recurrent Neural Networks

I. Szita; A. Lorincz

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arXiv · cs/0410004

Applying Policy Iteration for Training Recurrent Neural Networks

I. Szita·A. Lorincz

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Abstract

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.

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I. Szita, A. Lorincz. 2004-10-02. Applying Policy Iteration for Training Recurrent Neural Networks. https://arxiv.org/abs/cs/0410004

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