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.
Drift: A dynamic crowd inflow control system using lstm-based deep reinforcement learn- ing,
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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.