… deep reinforcement learning (BA-DRL) method for MG energy management under communication
failures. … ) to describe agents under communication failures, in which each agent can …

… a deep reinforcement learning (DRL)-… communication failure to stabilize traffic oscillations.
Specifically, the signalinterference-plus-noise ratio-based vehicle-to-vehicle communication is …

… Aerial communication NeTwork against random/targeted UAV failures under communication
… To solve the problem, we propose to leverage emerging deep reinforcement learning (DRL)…

… It can overcome the limitations of reinforcement learning, and thus open a … reinforcement
learning, namely Deep Reinforcement Learning (DRL). DRL embraces the advantage of Deep …

… -to-vehicle communications is proposed based on deep reinforcement learning (deep-RL).
The … extreme events which cause failure in URLLC-6G systems. The proposed experienced …

… It is unclear what are the constraints of this sharing methodology and its robustness when
communication channels can fail. RNN are also harder to train that simpler feed-forward …

Smart city communication networks hold the promise of … be supported across a communications
network has multiplied … maintain stable and reliable communications pathways. The end-…

… Taking these difficulties into account, we explore the possibility of utilizing deep
reinforcement learning (DRL) in automatic recovery. DRL has recently provided superhuman …

… Blockchain technology was used to prevent attacks and network communication failure while
sharing the collected data. Simulation results showed that compared to a random solution, …

… provides in wireless communications are: Model-free: Traditional communication engineering
adopts the model-based processing method, which can fail when mathematical model …