… - and learning-based paradigm for emergency networking in conditionally … for emergency
as well as for incumbent network traffic, is envisioned. Our paradigm for emergency networking …

… the emergency response in the immediate aftermath of a disaster, we propose a heuristic
multi-agent reinforcement learning … and volunteers from social network data and then schedule …

… So far, reinforcement learning has been successfully applied to solve problems such as
optimizing deep neural networks with asynchronous gradient descents for the controllers [11], …

… above that reinforcement learning can be a … emergency communications minimum sumrate
matters a lot. To overcome the presented challenges, we propose a reinforcement learning-…

… In the reinforcement learning domain, the term “deep” often means a set of recent … a NN
model using reinforcement learning, such as target network, replay buffer, duel network, etc. The …

… emergency networks. In this paper, the trajectory optimization for the air-ground cooperative
emergency networks … average spectrum efficiency of the emergency networks by multi-agent …

… If provided with an adequate emergency communication network, the information carried
by … However, it may not be possible for the emergency communication network to provide …

… relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning
… how to move each UAV in the network while maintaining connectivity between UAVs. The …

… it hard to capture the complexity and dynamics of flood emergencies. To overcome these
limitations, innovative approaches, such as reinforcement learning techniques can be used. …

… computing, we propose the ACIM method, introducing hierarchical reinforcement learning to
… Unstable emergency network links lead to interruptions during the transmission of large files …