  In recent years, attention has been focused on the relationship between
black-box optimiza- tion problem and reinforcement learning problem. In this
research, we propose the Mirror Descent Search (MDS) algorithm which is
applicable both for black box optimization prob- lems and reinforcement
learning problems. Our method is based on the mirror descent method, which is a
general optimization algorithm. The contribution of this research is roughly
twofold. We propose two essential algorithms, called MDS and Accelerated Mirror
Descent Search (AMDS), and two more approximate algorithms: Gaussian Mirror
Descent Search (G-MDS) and Gaussian Accelerated Mirror Descent Search (G-AMDS).
This re- search shows that the advanced methods developed in the context of the
mirror descent research can be applied to reinforcement learning problem. We
also clarify the relationship between an existing reinforcement learning
algorithm and our method. With two evaluation experiments, we show our proposed
algorithms converge faster than some state-of-the-art methods.
