Learning to OptimizeDownload PDF

24 Sep 2020 (modified: 04 Mar 2017)ICLR 2017 conference submissionReaders: 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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