Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold
Abstract
Solving a sequence of high-dimensional, nonconvex, but potentially similar optimization problems poses a computational challenge in engineering applications. We propose the first meta-learning framework that leverages the shared structure among sequential tasks to improve the computational efficiency and sample complexity of derivative-free optimization. Based on the observation that most practical high-dimensional functions lie on a latent low-dimensional manifold, which can be further shared among instances, our method jointly learns the meta-initialization of a search point and a meta-manifold. Theoretically, we establish the benefit of meta-learning in this challenging setting. Empirically, we demonstrate the effectiveness of the proposed algorithm in two high-dimensional reinforcement learning tasks.
Cite
Text
Sel et al. "Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.Markdown
[Sel et al. "Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.](https://mlanthology.org/l4dc/2023/sel2023l4dc-learningtolearn/)BibTeX
@inproceedings{sel2023l4dc-learningtolearn,
title = {{Learning-to-Learn to Guide Random Search: Derivative-Free Meta Blackbox Optimization on Manifold}},
author = {Sel, Bilgehan and Tawaha, Ahmad and Ding, Yuhao and Jia, Ruoxi and Ji, Bo and Lavaei, Javad and Jin, Ming},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
year = {2023},
pages = {38-50},
volume = {211},
url = {https://mlanthology.org/l4dc/2023/sel2023l4dc-learningtolearn/}
}