{"paper":{"title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andr\\'e Hottung, Cathy Wu, Changhyun Kwon, Chuanbo Hua, Davide Angioni, Fanchen Bu, Federico Berto, Fei Liu, Guojie Song, Haeyeon Kim, Haoran Ye, Hyeonah Kim, Jianan Zhou, Jiarui Wang, Jieyi Bi, Jie Zhang, Jinkyoo Park, Jiwoo Son, Joungho Kim, Junyoung Park, Kevin Tierney, Kijung Shin, Laurin Luttmann, Lin Xie, Minsu Kim, Nayeli Gast Zepeda, Qingfu Zhang, Sanghyeok Choi, Sungsoo Ahn, Wouter Kool, Yining Ma, Yu Hu, Zhiguang Cao","submitted_at":"2023-06-29T16:57:22Z","abstract_excerpt":"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 ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.17100","kind":"arxiv","version":6},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.17100/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}