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RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark. / Berto, Federico; Hua, Chuanbo; Park, Junyoung et al.
KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ed. / Luiza Antonie; Jian Pei; Xiaohui Yu. Association for Computing Machinery, 2025. p. 5278-5289 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Berto, F, Hua, C, Park, J, Luttmann, L, Ma, Y, Bu, F, Wang, J, Ye, H, Kim, M, Choi, S, Zepeda, NG, Hottung, A, Zhou, J, Bi, J, Hu, Y, Liu, F, Kim, H, Son, J, Kim, H, Angioni, D, Kool, W, Cao, Z, Zhang, Q, Kim, J, Zhang, J, Shin, K, Wu, C, Ahn, S, Song, G, Kwon, C, Tierney, K, Xie, L & Park, J 2025, RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark. in L Antonie, J Pei & X Yu (eds), KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2, Association for Computing Machinery, pp. 5278-5289, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, Toronto, Canada, 03.08.25. https://doi.org/10.1145/3711896.3737433

APA

Berto, F., Hua, C., Park, J., Luttmann, L., Ma, Y., Bu, F., Wang, J., Ye, H., Kim, M., Choi, S., Zepeda, N. G., Hottung, A., Zhou, J., Bi, J., Hu, Y., Liu, F., Kim, H., Son, J., Kim, H., ... Park, J. (2025). RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark. In L. Antonie, J. Pei, & X. Yu (Eds.), KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5278-5289). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2). Association for Computing Machinery. https://doi.org/10.1145/3711896.3737433

Vancouver

Berto F, Hua C, Park J, Luttmann L, Ma Y, Bu F et al. RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark. In Antonie L, Pei J, Yu X, editors, KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2025. p. 5278-5289. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3711896.3737433

Bibtex

@inbook{64dbbc32648d4fc4b90261a90283eb80,
title = "RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark",
abstract = "Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.",
keywords = "benchmark, combinatorial optimization, neural combinatorial optimization, open research community, reinforcement learning, Business informatics",
author = "Federico Berto and Chuanbo Hua and Junyoung Park and Laurin Luttmann and Yining Ma and Fanchen Bu and Jiarui Wang and Haoran Ye and Minsu Kim and Sanghyeok Choi and Zepeda, {Nayeli Gast} and Andr{\'e} Hottung and Jianan Zhou and Jieyi Bi and Yu Hu and Fei Liu and Hyeonah Kim and Jiwoo Son and Haeyeon Kim and Davide Angioni and Wouter Kool and Zhiguang Cao and Qingfu Zhang and Joungho Kim and Jie Zhang and Kijung Shin and Cathy Wu and Sungsoo Ahn and Guojie Song and Changhyun Kwon and Kevin Tierney and Lin Xie and Jinkyoo Park",
note = "Publisher Copyright: {\textcopyright} 2025 Association for Computing Machinery. All rights reserved.; 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025 ; Conference date: 03-08-2025 Through 07-08-2025",
year = "2025",
month = aug,
day = "3",
doi = "10.1145/3711896.3737433",
language = "English",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "5278--5289",
editor = "Luiza Antonie and Jian Pei and Xiaohui Yu",
booktitle = "KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "United States",

}

RIS

TY - CHAP

T1 - RL4CO

T2 - 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025

AU - Berto, Federico

AU - Hua, Chuanbo

AU - Park, Junyoung

AU - Luttmann, Laurin

AU - Ma, Yining

AU - Bu, Fanchen

AU - Wang, Jiarui

AU - Ye, Haoran

AU - Kim, Minsu

AU - Choi, Sanghyeok

AU - Zepeda, Nayeli Gast

AU - Hottung, André

AU - Zhou, Jianan

AU - Bi, Jieyi

AU - Hu, Yu

AU - Liu, Fei

AU - Kim, Hyeonah

AU - Son, Jiwoo

AU - Kim, Haeyeon

AU - Angioni, Davide

AU - Kool, Wouter

AU - Cao, Zhiguang

AU - Zhang, Qingfu

AU - Kim, Joungho

AU - Zhang, Jie

AU - Shin, Kijung

AU - Wu, Cathy

AU - Ahn, Sungsoo

AU - Song, Guojie

AU - Kwon, Changhyun

AU - Tierney, Kevin

AU - Xie, Lin

AU - Park, Jinkyoo

N1 - Publisher Copyright: © 2025 Association for Computing Machinery. All rights reserved.

PY - 2025/8/3

Y1 - 2025/8/3

N2 - Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.

AB - Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.

KW - benchmark

KW - combinatorial optimization

KW - neural combinatorial optimization

KW - open research community

KW - reinforcement learning

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=105014477776&partnerID=8YFLogxK

U2 - 10.1145/3711896.3737433

DO - 10.1145/3711896.3737433

M3 - Article in conference proceedings

AN - SCOPUS:105014477776

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 5278

EP - 5289

BT - KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

A2 - Antonie, Luiza

A2 - Pei, Jian

A2 - Yu, Xiaohui

PB - Association for Computing Machinery

Y2 - 3 August 2025 through 7 August 2025

ER -

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Neural Combinatorial Optimization on Heterogeneous Graphs: An Application to the Picker Routing Problem in Mixed-Shelves Warehouses

Luttmann, L. & Xie, L., 30.05.2024, In: Proceedings International Conference on Automated Planning and Scheduling, ICAPS. 34, p. 351-359 9 p.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks

Luttmann, L. & Mercorelli, P., 20.10.2021, 2021 25th International Conference on System Theory, Control and Computing (ICSTCC): October 20 – 23, 2021 Iași, ROMANIA, Proceedings. Ferariu, L., Matcovschi, M.-H. & Ungureanu, F. (eds.). Piscataway: Institute of Electrical and Electronics Engineers Inc., p. 234-241 8 p. (International Conference on System Theory, Control and Computing; no. 25).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses

Xie, L., Li, H. & Luttmann, L., 01.06.2023, In: European Journal of Operational Research . 307, 2, p. 713-730 18 p.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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