@InProceedings{8903067,
  author = {D. Baldazo and J. Parras and S. Zazo},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Decentralized Multi-Agent Deep Reinforcement Learning in Swarms of Drones for Flood Monitoring},
  year = {2019},
  pages = {1-5},
  abstract = {Multi-Agent Deep Reinforcement Learning is becoming a promising approach to the problem of coordination of swarms of drones in dynamic systems. In particular, the use of autonomous aircraft for flood monitoring is now regarded as an economically viable option and it can benefit from this kind of automation: swarms of unmanned aerial vehicles could autonomously generate nearly real-time inundation maps that could improve relief work planning. In this work, we study the use of Deep Q-Networks (DQN) as the optimization strategy for the trajectory planning that is required for monitoring floods, we train agents over simulated floods in procedurally generated terrain and demonstrate good performance with two different reward schemes.},
  keywords = {autonomous aerial vehicles;control engineering computing;emergency management;floods;learning (artificial intelligence);multi-agent systems;optimisation;trajectory control;relief work planning;Deep Q-Networks;flood monitoring;dynamic systems;autonomous aircraft;real-time inundation maps;decentralized multiagent deep reinforcement learning;drones swarms coordination problem;unmanned aerial vehicles swarms;optimization strategy;trajectory planning;Aircraft;Training;Floods;Reinforcement learning;Atmospheric modeling;Europe;Signal processing;navigation;reinforcement learning;swarms;decentralized control;floods},
  doi = {10.23919/EUSIPCO.2019.8903067},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533953.pdf},
}

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In particular, the use of autonomous aircraft for flood monitoring is now regarded as an economically viable option and it can benefit from this kind of automation: swarms of unmanned aerial vehicles could autonomously generate nearly real-time inundation maps that could improve relief work planning. 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Baldazo and J. Parras and S. Zazo},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Decentralized Multi-Agent Deep Reinforcement Learning in Swarms of Drones for Flood Monitoring},\n year = {2019},\n pages = {1-5},\n abstract = {Multi-Agent Deep Reinforcement Learning is becoming a promising approach to the problem of coordination of swarms of drones in dynamic systems. In particular, the use of autonomous aircraft for flood monitoring is now regarded as an economically viable option and it can benefit from this kind of automation: swarms of unmanned aerial vehicles could autonomously generate nearly real-time inundation maps that could improve relief work planning. 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