  To achieve scenario intelligence, humans must transfer knowledge to robots by
developing goal-oriented algorithms, which are sometimes insensitive to
dynamically changing environments. While deep reinforcement learning achieves
significant success recently, it is still extremely difficult to be deployed in
real robots directly. In this paper, we propose a hybrid structure named
Option-Interruption in which human knowledge is embedded into a hierarchical
reinforcement learning framework. Our architecture has two key components:
options, represented by existing human-designed methods, can significantly
speed up the training process and interruption mechanism, based on learnable
termination functions, enables our system to quickly respond to the external
environment. To implement this architecture, we derive a set of update rules
based on policy gradient methods and present a complete training process. In
the experiment part, our method is evaluated in Four-room navigation and
exploration task, which shows the efficiency and flexibility of our framework.
