The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data
Summary: RLR-Tree uses reinforcement learning to guide subtree selection and node splitting in an R-Tree, without changing its structure. The learned rules beat classical heuristics on 100M objects, reducing query times while preserving R-Tree compatibility. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Tu Gu (Nanyang Technological University)
- 2. Kaiyu Feng (Nanyang Technological University)
- 3. Gao Cong (Nanyang Technological University)
- 4. Cheng Long (Nanyang Technological University)
- 5. Zheng Wang (Nanyang Technological University)
- 6. Sheng Wang (Alibaba)
BibTeX Citation
@inproceedings{gu_sigmod23,
title = {{The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data}},
author = {Gu, Tu and Feng, Kaiyu and Cong, Gao and Long, Cheng and Wang, Zheng and Wang, Sheng},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588917},
url = {https://dl.acm.org/doi/10.1145/3588917},
year = {2023}
}
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