… propose a reinforcement-learning-based routing protocol. Reinforcement-learning-based
routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search …

… , we propose deep reinforcement learning (DRL) … wireless networks for smart cities. For this
purpose, we use one of the DRL models, Q-learning, for MAC-RA in dense wireless networks …

… In this paper, we advocate the use of reinforcement learning to address the issue of … use
of reinforcement learning for task adaptation and scheduling in wireless sensor networks. We …

… 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 …

… We develop a deep reinforcement learning solution for nodes to decide on whether to
participate in communication, defend the network, or attack other transmissions for the sake of …

… We consider a wireless network containing N correlated channels whose states can be
either vacant (0) or occupied (1). The joint state transition of these channels follows a 2N -states …

… In this paper, one reinforcement learning method, Q-Learning… retransmissions in single-hop
networks. A similar approach has … of the application of reinforcement learning to the medium …

… reinforcement learning for RRAM in wireless networks, we included the following terms during
the search stage along with ”AND/OR” combinations of them; ”deep reinforcement learning…

… This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning
algorithm that is at once scalable and model-free. We compare our scheduling algorithm to …

… For example, we know that the expected transmission time of a packet in a wireless
network is 20 minutes. However, this information may not be so meaningful because it may …