Application of reinforcement learning to stacked autoencoder deep network architecture optimization

Abstract

In this work, a new algorithm for the structure optimization of stacked autoencoder deep network (SADN) is introduced. It relies on the search for the numbers of the neurons in the first and the second layer of SADN through an approach based on reinforcement learning (RL). The Q(0)-learning based agent is constructed, which according to received reinforcement signal, picks appropriate values for the neurons. Considered network, with the architecture adjusted by the proposed algorithm, is applied to the task of MNIST digit database recognition. The classification quality is computed for SADN to determine its performance. It is shown that, using the proposed algorithm, the semi-optimal configuration of the number of hidden neurons can be achieved much faster than the successive exploration of the entire space of layers’ arrangement.