… -driven deep reinforcement learning assisted resource allocation method. We first design
a novel deep … Then a novel channel information absent Q-learning resource allocation (CIAQ) …

… -efficient resource allocation in CRANs with deep reinforcement … deep neural network (DNN)
to approximating the action-value function, and formally formulate the resource allocation …

… use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in
… and dynamic resource allocation in MEC Computing, optimizing allocation decisions based …

… This shows that the deep reinforcement learning technique used in this paper is able to
learn and optimize resource allocation even if the utility models are non-convex. …

… more intelligent and efficient resource allocation framework to … deep reinforcement
learning-based resource allocation framework is developed to jointly optimize the channel allocation …

… the offloading decision of each task and the resource allocation (eg, CPU computing power)
… To address the issue, we propose a deep reinforcement learning (DRL) framework based …

… In industry, deep reinforcement learning (DRL) is increasingly used in robotics, job shop …
resource allocation for industrial edge devices. An optimal usage of available resources of the …

… -driven algorithm based on the Deep Reinforcement Learning (DRL)… of optimizing
computational resource allocation to achieve … Specifically, we adopt the deep reinforcement …

… CONCLUSION In this paper, we proposed FL-DLT3, which is a new deep reinforcement
learning based resource allocation with FL for EdgeIoT. Given the large state and action space, …

… resources shall be correspondingly managed in an adaptive way. Traditional model-based
resource … DRL approach to efficiently manage the resources at the network edge. Following …