RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909695087738880 |
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| author | Berto, Federico Hua, Chuanbo Park, Junyoung Luttmann, Laurin Ma, Yining Bu, Fanchen Wang, Jiarui Ye, Haoran Kim, Minsu Choi, Sanghyeok Zepeda, Nayeli Gast Hottung, André Zhou, Jianan Bi, Jieyi Hu, Yu Liu, Fei Kim, Hyeonah Son, Jiwoo Kim, Haeyeon Angioni, Davide Kool, Wouter Cao, Zhiguang Zhang, Qingfu Kim, Joungho Zhang, Jie Shin, Kijung Wu, Cathy Ahn, Sungsoo Song, Guojie Kwon, Changhyun Tierney, Kevin Xie, Lin Park, Jinkyoo |
| author_facet | Berto, Federico Hua, Chuanbo Park, Junyoung Luttmann, Laurin Ma, Yining Bu, Fanchen Wang, Jiarui Ye, Haoran Kim, Minsu Choi, Sanghyeok Zepeda, Nayeli Gast Hottung, André Zhou, Jianan Bi, Jieyi Hu, Yu Liu, Fei Kim, Hyeonah Son, Jiwoo Kim, Haeyeon Angioni, Davide Kool, Wouter Cao, Zhiguang Zhang, Qingfu Kim, Joungho Zhang, Jie Shin, Kijung Wu, Cathy Ahn, Sungsoo Song, Guojie Kwon, Changhyun Tierney, Kevin Xie, Lin Park, Jinkyoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_17100 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark Berto, Federico Hua, Chuanbo Park, Junyoung Luttmann, Laurin Ma, Yining Bu, Fanchen Wang, Jiarui Ye, Haoran Kim, Minsu Choi, Sanghyeok Zepeda, Nayeli Gast Hottung, André Zhou, Jianan Bi, Jieyi Hu, Yu Liu, Fei Kim, Hyeonah Son, Jiwoo Kim, Haeyeon Angioni, Davide Kool, Wouter Cao, Zhiguang Zhang, Qingfu Kim, Joungho Zhang, Jie Shin, Kijung Wu, Cathy Ahn, Sungsoo Song, Guojie Kwon, Changhyun Tierney, Kevin Xie, Lin Park, Jinkyoo Machine Learning Artificial Intelligence 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. |
| title | RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2306.17100 |