Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System

TL;DR

This paper introduces HRL4AG, a hierarchical reinforcement learning framework that improves cooperative air-ground delivery efficiency and scalability in urban logistics by decomposing decision spaces and addressing heterogeneity.

Contribution

The paper presents a novel HRL framework with a high-level manager and mode-specific workers, plus an internal reward mechanism, to enhance scalability and handle heterogeneity in UAV-ground delivery systems.

Findings

Improves delivery success rate by up to 26%.

Achieves 80-fold increase in computational efficiency.

Outperforms state-of-the-art baselines in experiments.

Abstract

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.