摘要：Options have been shown to be a key step in extendingreinforcement learning beyond low-level reactionary sys-tems to higher-level, planning systems. Most of the op-tions research involves hand-crafted options; there hasbeen only very limited work in the automated discoveryof options. We extend early work in automated optiondiscovery with a .exible and robust method
