Deep Reinforcement Learning Enabled Multi-UAV Scheduling for Disaster Data Collection With Time-Varying Value

The congestion and disruption of information infrastructures frequently happen during disasters, which would hinder the understanding of disaster scenarios, and thus impede rapid response activities. With the advantages of high flexibility and efficiency, this paper proposes to use UAVs as temporary and mobile relays for disaster data collection. However, different from many existing data collection scenarios in industrial sectors, the disaster data value varies with UAV arrival time and service time in terms of their importance for disaster response, which makes the scheduling of UAVs challenging. To address such a problem, this paper proposes an attention-based Deep Reinforcement Learning (DRL) method for multi-UAV scheduling considering time-varying data value. Specifically, the problem is modeled as a specific team orienteering problem with time-varying value. Then the relationships between UAV route selection and service time at each node are analyzed, based on which the computing efficiency for solution algorithms can be improved. After that, an attention-based DRL method is developed, with a calibrated attention model and decoding method. Finally, systematic computational experiments are conducted to evaluate the performance of the proposed method, which demonstrates its superiority over popular methods in UAV scheduling, especially for large-scale and complex scenarios.