  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.
