… to satisfy the requirements of mobile devices under the changeable MEC … , Deep Reinforcement
Learning based Resource Allocation (DRLRA) scheme, which can allocate computing …

… Towards this end, we propose a deep reinforcement learning-based online offloading (DROO)
framework to maximize the weighted sum of the computation rates of all the WDs, ie, the …

… tasks as well as edge load dynamics, and formulate a task offloading problem to minimize
the expected long-term cost. We propose a model-free deep reinforcement learning-based …

… enabled mobile-edge computing: A deep reinforcement learning … for mobile edge computing
by deep reinforcement learning … mobile edge computing via deep reinforcement learning for …

… To address the first technical challenge in Remark 2, we adopt a model-free reinforcement
learning scheme called Qlearning [10], which allows us to learn the optimal control policy …

… In this paper, we investigate mobile edge computing (MEC) … some computational tasks
assisted by multiple computational … the deep reinforcement learning algorithm. In this algorithm, …

… mobile fog, we apply the distributed reinforcement learning to solve the code offloading issue
of mobile fog. … offload codes: (1) mobile fog in neighboring mobile stations O 1 . (2) the near …

… Smart cities can benefit from offloading to edge points in the … mobile edge computing
networks (MECNs) in more than one region, and they consist of multiple access points, multi-edge …

… In this work, we propose a deep reinforcement learning approach for delay-… learning
scheme jointly optimizes the offloading servers and computational frequency allocation of edge …

… ), we propose a deep Reinforcement leArning based trajectory control algorithm (RAT). In
RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the …