Learning to Optimize
This addresses the laborious process of algorithm design for researchers and practitioners, offering a novel automated approach.
The paper tackles automating algorithm design by learning an optimization algorithm through reinforcement learning, demonstrating that it outperforms existing hand-engineered algorithms in convergence speed and final objective value.
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, which we believe to be the first method that can automatically discover a better 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.