  This paper proposes a new optimizer for deep learning, named d-AmsGrad. In
the real-world data, noise and outliers cannot be excluded from dataset to be
used for learning robot skills. This problem is especially striking for robots
that learn by collecting data in real time, which cannot be sorted manually.
Several noise-robust optimizers have therefore been developed to resolve this
problem, and one of them, named AmsGrad, which is a variant of Adam optimizer,
has a proof of its convergence. However, in practice, it does not improve
learning performance in robotics scenarios. This reason is hypothesized that
most of robot learning problems are non-stationary, but AmsGrad assumes the
maximum second momentum during learning to be stationarily given. In order to
adapt to the non-stationary problems, an improved version, which slowly decays
the maximum second momentum, is proposed. The proposed optimizer has the same
capability of reaching the global optimum as baselines, and its performance
outperformed that of the baselines in robotics problems.
