Macro-action Discovery Based on Change Point Detection and Boosting

We present a novel approach to automatic macroaction discovery and its application to a complex goal-planning task. The problem of macro-action discovery is framed as one of multiple change point detection and is addressed with the help of the Dynamic Programming Boosting algorithm. The procedure is then employed to solve a complex goal-planning problem which entails an avatar navigating a 3D environment. By using DPBoost to decompose the problem into a number of simpler ones, we are able to successfully address both the complexity and partial observability of the environment.