Improved Automatic Discovery of Subgoals for Options in Hierarchical Reinforcement Learning

Abstract

Options have been shown to be a key step in extending reinforcement learning beyond low-level reactionary systems to higher-level, planning systems. Most of the options research involves hand-crafted options; there has been only very limited work in the automated discovery of options. We extend early work in automated option discovery with a fle xible and robust method.