TY - GEN
T1 - Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System
AU - Lei, Songxin
AU - Ma, Chunming
AU - Wen, Haomin
AU - Li, Yexin
AU - Chen, Lizhenghe
AU - Yang, Qianyu
AU - Tsung, Fugee
AU - Chen, Lei
AU - Ruan, Sijie
AU - Liang, Yuxuan
N1 - Publisher Copyright:
© 2026 Owner/Author.
PY - 2026/8/8
Y1 - 2026/8/8
N2 - 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. Our source codes are available at https://github.com/thunderlrr/Cooperative-Air-Ground-Delivery.
AB - 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. Our source codes are available at https://github.com/thunderlrr/Cooperative-Air-Ground-Delivery.
KW - ai
KW - cooperative air-ground delivery
KW - hierarchical reinforcement learning
KW - order dispatch
KW - urban computing
UR - https://www.scopus.com/pages/publications/105051058752
U2 - 10.1145/3770855.3818964
DO - 10.1145/3770855.3818964
M3 - Conference contribution
AN - SCOPUS:105051058752
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 11295
EP - 11306
BT - KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
PB - Association for Computing Machinery
T2 - 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026
Y2 - 9 August 2026 through 13 August 2026
ER -