---
title: "Deep Reinforcement Learning with Hierarchical Structures"
authors:
  - "Siyuan Li"
doi: "10.24963/ijcai.2021/681"
url: "https://ijcai.org/proceedings/2021/681"
pdf: "https://ijcai.org/proceedings/2021/0681.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4899-4900"
track: "Doctoral Consortium"
keywords:
  - "Machine Learning: Reinforcement Learning"
---

# Deep Reinforcement Learning with Hierarchical Structures

Siyuan Li

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4899-4900. DOI: [10.24963/ijcai.2021/681](https://doi.org/10.24963/ijcai.2021/681)

## Abstract

Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.