Learning to OptimizeDownload PDF

22 Mar 2023, 22:26 (modified: 21 Jul 2022, 19:53)ICLR 2017 PosterReaders: Everyone
TL;DR: We explore learning an optimization algorithm automatically.
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.
Keywords: Reinforcement Learning, Optimization
Conflicts: eecs.berkeley.edu
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