A Data-driven Spatiotemporal Simulator for Reinforcement Learning Methods
Summary: Introduces DSS, a data-driven spatiotemporal simulator for training and validating RL in taxi dispatch and warehouse scheduling. Distinct in offering extensible scenario plugins, visualization and developer tools to streamline algorithm design on real spatiotemporal traces. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Dingyuan Shi (Beihang University)
- 2. Bingchen Song (Beijing Institute of Technology)
- 3. Yuanyuan Zhang (North China Institute of Computing Technology)
- 4. Haolong Yang (Beihang University)
- 5. Ke Xu (Beihang University)
BibTeX Citation
@article{shi_vldb24,
title = {{A Data-driven Spatiotemporal Simulator for Reinforcement Learning Methods}},
author = {Shi, Dingyuan and Song, Bingchen and Zhang, Yuanyuan and Yang, Haolong and Xu, Ke},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4257--4260},
doi = {10.14778/3685800.3685849},
url = {https://doi.org/10.14778/3685800.3685849},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next