A learning-based controller for coordinating humans, drones, and ground vehicles in emergency sensing claims an average 18.42% increase in task completion rate over baseline methods.
Energy-efficient ground-air-space vehicular crowdsensing by hierarchical multi-agent deep reinforcement learning with diffusion models,
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A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue
A learning-based controller for coordinating humans, drones, and ground vehicles in emergency sensing claims an average 18.42% increase in task completion rate over baseline methods.