Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness
This addresses disaster response coordination for rescue operations, though it appears incremental as it applies existing MARL methods to a new application domain.
The paper tackles the problem of creating real-time 3D maps for disaster situational awareness by using a Multi-Agent Reinforcement Learning (MARL) framework to coordinate human bystanders and drones via smartphones, with evaluation through simulations and real experiments showing effectiveness in tracking environmental dynamics.
Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. Via both simulations and real experiments, an evaluation of the framework in terms of effectiveness in tracking random dynamicity of the environment is presented.