We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as best practices in implementation, such as modularity and configuration management, to be efficient and easily modifiable by researchers for adaptations of neural network architecture, environments, and RL algorithms. Contrary to the existing focus on specific tasks like the traveling salesman problem (TSP) for performance assessment, we underline the importance of scalability and generalization capabilities for diverse CO tasks. We also systematically benchmark zero-shot generalization, sample efficiency, and adaptability to changes in data distributions of various models. Our experiments show that some recent SOTA methods fall behind their predecessors when evaluated using these metrics, suggesting the necessity for a more balanced view of the performance of neural CO (NCO) solvers. We hope RL4CO will encourage the exploration of novel solutions to complex real-world tasks, allowing the NCO community to compare with existing methods through a standardized interface that decouples the science from software engineering. We make our library publicly available at https://github.com/kaist-silab/rl4co.
View on arXiv@article{berto2025_2306.17100,
title={ RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark },
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 Nayeli Gast Zepeda and André 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 },
journal={arXiv preprint arXiv:2306.17100},
year={ 2025 }
}