Deep Reinforcement Learning with Hierarchical Structures

Siyuan Li

IJCAI 2021poster0 citations

Deep Reinforcement Learning with Hierarchical Structures

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.

Machine Learning: Reinforcement Learning

BibTeX

@inproceedings{ijcai2021p681,
  title     = {Deep Reinforcement Learning with Hierarchical Structures},
  author    = {Li, Siyuan},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4899--4900},
  year      = {2021},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2021/681},
  url       = {https://doi.org/10.24963/ijcai.2021/681},
}