… 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 …

… for future energy-constrained wireless networks. In this letter, … In this system, we propose a
reinforcement learning based … , we propose a new reinforcement learning algorithm and the …

… -wake and task schedulers, in wireless networks, as well as the … scheduling schemes in
wireless networks in order to explore … work topologies and wireless networks, as well as the chal- …

… that change how the network reacts over time. To that … reinforcement learning techniques
to switch the MAC protocol in structured wireless networks according to the ongoing network …

… 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 …

… 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 …

… 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 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 …

… reinforcement learning (DRL)-based methods to optimize beamforming at the uplink of a
cell-free network… uplink beamforming method (ie centralized learning) that uses the Deep …