In this article, we address the issues of stability and data-efficiency in reinforcement learning (RL). A novel RL approach, Kullback-Leibler divergence-regularized distributional (KL-C51) is proposed to integrate advantages both (KL) one framework. KL-C51 derived Bellman equation TD errors regularized by KL divergence a perspective explored approximated strategies properly mapping correspondin...

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify measures such as Kullback-Leibler information, J-divergence, Hellinger distance, -Divergence, … and so on. Properties and results related to distance between probability d...

Recently Habibi et al. (2006) defined a pre-experimental criterion for the potential strength of evidence provided by an experiment, based on Kullback-Leibler distance. In this paper, we investigate the potential statistical evidence in an experiment in terms of Renyi distance and compare the potential statistical evidence in lower (upper) record values with that in the same number of ii...

In this paper, we obtain the asymptotic forms of the generalization errors for some three layered learning models in Bayesian estimation. The generalization error measures how precisely learning models can approximate true density functions which produce learning data. We use a recursive blowing up process for analyzing the Kullback function of the learning model. Then, we have the maximum pole...