Deep Reinforcement-Learning Framework for Exploratory Data Analysis

Deep Reinforcement Learning (DRL) is unanimously considered as a breakthrough technology, used in solving a growing number of AI challenges previously considered to be intractable. In this work, we aim to set the ground for employing DRL techniques in the context of Exploratory Data Analysis (EDA), an important yet challenging, that is critical in many application domains. We suggest an end-to-end framework architecture, coupled with an initial implementation of each component. The goal of this short paper is to encourage the exploration of DRL models and techniques for facilitating a full-fledged, autonomous solution for EDA.