UAV Carrier Enabled Vehicular Crowdsensing by Multi-Agent Reinforcement Learning with Mutual Policy Divergence and Attentive Memory Update

Vehicular Crowdsensing (VCS) has emerged as a promising paradigm that leverages the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for large-scale urban sensing and data collection. In this paper, we consider a UAV-carrier-enabled VCS campaign in which UGVs dynamically dispatch and recall UAVs within the workzone, where UAVs sense points of interest (PoIs) and UGVs facilitate data collection, with the goal of maximizing the total collected data volume and geographic fairness, while minimizing overall energy consumption. We propose a heterogeneous multi-agent deep reinforcement learning (MADRL) framework, called “HADRL-VCS”, consisting of an attentive memory-integrated information exchange mechanism that enables UAVs and UGVs to fuse newly received information with historical memory, thereby expanding the collective sensing range and enhancing cooperative decision-making. We also propose a mutual policy divergence-driven exploration strategy designed to explicitly promote diverse exploration and complementary role differentiation among heterogeneous UAVs and UGVs. Extensive experimental results based on realistic simulations using real-world urban maps from Guangzhou, China, and Madrid, Spain, show that HADRL-VCS achieves better performance over five baselines in terms of data collection ratio, geographic fairness, sensing range expansion ratio, overlap ratio, and efficiency.