  Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
