Learning to Optimize
Abstract
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
Cite
Text
Li and Malik. "Learning to Optimize." International Conference on Learning Representations, 2017.Markdown
[Li and Malik. "Learning to Optimize." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/li2017iclr-learning/)BibTeX
@inproceedings{li2017iclr-learning,
title = {{Learning to Optimize}},
author = {Li, Ke and Malik, Jitendra},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/li2017iclr-learning/}
}