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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.12913 |
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| _version_ | 1866910021509447680 |
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| author | Lei, Songxin Ma, Chunming Wen, Haomin Li, Yexin Chen, Lizhenghe Yang, Qianyu Tsung, Fugee Chen, Lei Ruan, Sijie Liang, Yuxuan |
| author_facet | Lei, Songxin Ma, Chunming Wen, Haomin Li, Yexin Chen, Lizhenghe Yang, Qianyu Tsung, Fugee Chen, Lei Ruan, Sijie Liang, Yuxuan |
| contents | Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments on two real-world datasets and an evaluation platform demonstrate that HRL4AG significantly outperforms state-of-the-art baselines, improving the delivery success rate by up to 26% while achieving an 80-fold increase in computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12913 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System Lei, Songxin Ma, Chunming Wen, Haomin Li, Yexin Chen, Lizhenghe Yang, Qianyu Tsung, Fugee Chen, Lei Ruan, Sijie Liang, Yuxuan Computers and Society Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments on two real-world datasets and an evaluation platform demonstrate that HRL4AG significantly outperforms state-of-the-art baselines, improving the delivery success rate by up to 26% while achieving an 80-fold increase in computational efficiency. |
| title | Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2602.12913 |