Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning.
Paul J. Pritz|Liang Ma|Kin K. Leung
| Anthology ID: | DBLP:conf/cikm/Pritz0L21 |
|---|---|
| Volume: | CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021 |
| Year: | 2021 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 1447-1456 |
| URL: | https://doi.org/10.1145/3459637.3482357 |
| DOI: | https://doi.org/10.1145/3459637.3482357 |
| DBLP: | conf/cikm/Pritz0L21 |
| BibTeX: |
@inproceedings{pritz-2021-jointlylearned,
author = {Paul J. Pritz and
Liang Ma and
Kin K. Leung},
editor = {Gianluca Demartini and
Guido Zuccon and
J. Shane Culpepper and
Zi Huang and
Hanghang Tong},
title = {{Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning}},
booktitle = {{CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021}},
pages = {1447--1456},
publisher = {ACM},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482357},
doi = {https://doi.org/10.1145/3459637.3482357}
}