  Hierarchical reinforcement learning (HRL) has recently shown promising
advances on speeding up learning, improving the exploration, and discovering
intertask transferable skills. Most recent works focus on HRL with two levels,
i.e., a master policy manipulates subpolicies, which in turn manipulate
primitive actions. However, HRL with multiple levels is usually needed in many
real-world scenarios, whose ultimate goals are highly abstract, while their
actions are very primitive. Therefore, in this paper, we propose a
diversity-driven extensible HRL (DEHRL), where an extensible and scalable
framework is built and learned levelwise to realize HRL with multiple levels.
DEHRL follows a popular assumption: diverse subpolicies are useful, i.e.,
subpolicies are believed to be more useful if they are more diverse. However,
existing implementations of this diversity assumption usually have their own
drawbacks, which makes them inapplicable to HRL with multiple levels.
Consequently, we further propose a novel diversity-driven solution to achieve
this assumption in DEHRL. Experimental studies evaluate DEHRL with five
baselines from four perspectives in two domains; the results show that DEHRL
outperforms the state-of-the-art baselines in all four aspects.
