  Many complex real-world tasks are composed of several levels of sub-tasks.
Humans leverage these hierarchical structures to accelerate the learning
process and achieve better generalization. In this work, we study the inductive
bias and propose Ordered Memory Policy Network (OMPN) to discover subtask
hierarchy by learning from demonstration. The discovered subtask hierarchy
could be used to perform task decomposition, recovering the subtask boundaries
in an unstruc-tured demonstration. Experiments on Craft and Dial demonstrate
that our modelcan achieve higher task decomposition performance under both
unsupervised and weakly supervised settings, comparing with strong baselines.
OMPN can also bedirectly applied to partially observable environments and still
achieve higher task decomposition performance. Our visualization further
confirms that the subtask hierarchy can emerge in our model.
