… joint user association and resource allocation issue. In this paper, a reinforcement learning
(… guaranteeing the quality of service requirements of user equipments (UEs) in the downlink …

… as user association and resource allocation (UARA). The problem of user association was
… Here, reinforcement learning approach is applied to solve the joint optimization problem of …

… in the user association problem for AmBC-based SRN. The base station (BS) in the primary
network serves the cellular users … In this paper, we consider the user association policy is …

… analysis, as deep reinforcement learning shows great potential in handling large systems,
in this paper, a multi-agent deep reinforcement learning for joint user association and power …

… The aim is to minimize the end-to-end delay of users … Deep Q-Network (DQN)-based
algorithm. The simulation results demonstrate that our proposed scheme reduces the average user …

… and applying traditional deep reinforcement approaches such as deep Q learning, we propose
working on the hybrid space directly by using the novel parameterized deep Q-network (P…

… learning to support scalability by replacing the reward table with deep neural networks. This
… to apply deep reinforcement learning to solve the resource allocation and user association …

… an online deep reinforcement learning (DRL) … deep neural networks (DNNs) can generate
user association solutions. We use a shared memory structure to store the best association …

… to make use of a Deep Reinforcement Learning (DRL) technique based on Deep QNetworks
(DQN) in order to solve this challenging problem. Namely, the mobile users autonomously …

… on multi-agent reinforcement learning (MARL) for user association in heterogeneous network
[8]. … , we propose a distributed deep MARL framework for user association to maximize the …