Online Adaptation of Deep Architectures with Reinforcement Learning

TL;DR

This paper introduces an online learning method that dynamically adapts deep neural network architectures using reinforcement learning to handle changing data distributions effectively.

Contribution

It proposes an online stacked Denoising Autoencoder that adapts its structure via reinforcement learning to improve responsiveness and robustness in non-stationary environments.

Findings

Outperforms existing methods in non-stationary data scenarios

Better preserves prior knowledge during adaptation

More responsive and robust to distribution changes

Abstract

Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning. However, adaptation to changes in the data distribution (also known as covariate shift) needs to be performed without compromising past knowledge already built in into the model to cope with voluminous and dynamic data. In this paper, we propose an online stacked Denoising Autoencoder whose structure is adapted through reinforcement learning. Our algorithm forces the network to exploit and explore favourable architectures employing an estimated utility function that maximises the accuracy of an unseen validation sequence. Different actions, such as Pool, Increment and Merge are available to modify the structure of the network. As we observe through a series of experiments, our approach is more responsive, robust, and principled than its counterparts for non-stationary as well as stationary data distributions. Experimental results indicate that our algorithm performs better at preserving gained prior knowledge and responding to changes in the data distribution.