… a novel deep reinforcement learning approach [27], which is used to obtain the resource
allocation policy in green heterogeneous wireless networks with integrated networking, caching…

… In this paper, we propose to use reinforcement learning, namely Q-learning algorithm to …
-radio wireless mesh networks. In the proposed mechanism (RLBDR), reinforcement-learning is …

… -reinforcement-learning (DRL)-based unsupervised wireless-… to model a continuous
wireless-localization process as a … landmark data from unlabeled wireless received signal …

… learning techniques in wireless networks. We emphasize how these tools can be used in
the learning … to mixing network simulators, reinforcement learning, and wireless protocols. …

… wireless sensor networks is to enable each node to harvest energy in its environment. To …
A novel energy management algorithm based on reinforcement learning (RLMan) is proposed …

… access policy based on deep reinforcement learning algorithm to optimally select the channel
… Thus, the fully distributed reinforcement learning will be explored for channel access in our …

… In this paper, with the aim to design an autonomous wireless video streaming system, we
apply the deep reinforcement learning approach to dynamic resource optimization for wireless …

… According to these limitations, this study aims to present a multi-path routing strategy for
wireless sensor networks based on reinforcement learning for network life optimization [14, 15]. …

… solution for data collection of large-scale wireless sensor networks (WSNs). In this paper, …
reinforcement learning (DRL) technique, pointer networkA* (Ptr-A*), which can efficiently learn …

… We introduce the reinforcement learning algorithm to dynamically learn this information,
and all of the information will be captured by the reinforcement learning model of the routing …