Deep Reinforcement Learning with Hierarchical Structures
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4899-4900.
https://doi.org/10.24963/ijcai.2021/681
Keywords
- Machine Learning: Reinforcement Learning
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.
Siyuan Li. 2021. Deep Reinforcement Learning with Hierarchical Structures. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4899–4900. https://doi.org/10.24963/ijcai.2021/681
Li, S. (2021). Deep Reinforcement Learning with Hierarchical Structures. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 (pp. 4899–4900). IJCAI. https://doi.org/10.24963/ijcai.2021/681
@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 = {Prof. 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},
}
TY - CPAPER TI - Deep Reinforcement Learning with Hierarchical Structures AU - Li, Siyuan T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 4899 EP - 4900 DO - 10.24963/ijcai.2021/681 UR - https://doi.org/10.24963/ijcai.2021/681 ER -Copied
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