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...

Stochastic interdependence of a probablility distribution on a product space is measured by its Kullback-Leibler distance from the exponential family of product distributions (called multi-information). Here we investigate lowdimensional exponential families that contain the maximizers of stochastic interdependence in their closure. Based on a detailed description of the structure of probablili...