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Full-Text Articles in Physical Sciences and Mathematics
An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model For Enhancing Uav Landing On Moving Targets, Najmaddin Abo Mosali, Syariful Syafiq Shamsudin, Salama A. Mostafa, Omar Alfandi, Rosli Omar, Najib Al-Fadhali, Mazin Abed Mohammed, R. Q. Malik, Mustafa Musa Jaber, Abdu Saif
An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model For Enhancing Uav Landing On Moving Targets, Najmaddin Abo Mosali, Syariful Syafiq Shamsudin, Salama A. Mostafa, Omar Alfandi, Rosli Omar, Najib Al-Fadhali, Mazin Abed Mohammed, R. Q. Malik, Mustafa Musa Jaber, Abdu Saif
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The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which …
Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim
Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim
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The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, …