Title: Statistical Mechanics of Node-perturbation Learning with Noisy Baseline,
Abstract: Node-perturbation learning is a type of statistical gradient descent
algorithm that can be applied to problems where the objective function is not
explicitly formulated, including reinforcement learning. It estimates the
gradient of an objective function by using the change in the object function in
response to the perturbation. The value of the objective function for an
unperturbed output is called a baseline. Cho et al. proposed node-perturbation
learning with a noisy baseline. In this paper, we report on building the
statistical mechanics of Cho's model and on deriving coupled differential
equations of order parameters that depict learning dynamics. We also show how
to derive the generalization error by solving the differential equations of
order parameters. On the basis of the results, we show that Cho's results are
also apply in general cases and show some general performances of Cho's model.