# Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

arXiv: https://arxiv.org/abs/1709.04579 · HTML: https://mlsanity.com/paper/1709.04579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Learning Parameterized Task Structure for Generalization to Unseen Entities](https://mlsanity.com/paper/2203.15034/) (2022)
- [Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization](https://mlsanity.com/paper/2205.12648/) (2022)

## Abstract (arXiv)

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.