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Complex Task Assignment of Aviation Emergency Rescue Based on Multiagent Reinforcement Learning

In: City, Society, and Digital Transformation

Author

Listed:
  • Che Shen

    (The Hong Kong University of Science and Technology)

  • Xianbing Wang

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Emergency rescue is a powerful countermeasure to disasters, among which Aviation Emergency Rescue (AER) is irreplaceable thanks to its unique aviation attribute. However, traditional optimization methods are not capable of the dynamic task allocation of AER. This study performs a Multiagent Reinforcement Learning (MARL) model to handle the complex task assignment problem faced in AER, carries out a detailed analysis of the problem, and do comparative experiments with the Nearby policy and Best-fit policy. The result shows that the MARL model outperforms other simple models in AER.

Suggested Citation

  • Che Shen & Xianbing Wang, 2022. "Complex Task Assignment of Aviation Emergency Rescue Based on Multiagent Reinforcement Learning," Lecture Notes in Operations Research, in: Robin Qiu & Wai Kin Victor Chan & Weiwei Chen & Youakim Badr & Canrong Zhang (ed.), City, Society, and Digital Transformation, chapter 0, pages 345-355, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-15644-1_26
    DOI: 10.1007/978-3-031-15644-1_26
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