… 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. …

… networks. In this paper, we develop a distributed routing strategy based on deep
reinforcement learning that generalizes to diverse traffic patterns, congestion levels, network …

… 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]. …

… a reinforcement learning algorithm. This algorithm is able to follow unexpected changes and
traffic bursts rapidly. Through the learning … that uses a reinforcement learning algorithm. In …

We address the problem of cooperative retransmission in the media access control (MAC)
layer of a distributed wireless network with spatial reuse, where there can be multiple …

… 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 …

… aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing
… next-generation wireless networks. To this end, we propose a reinforcement learning-based …

… 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 …

… deep reinforcement learning for intelligent resource management in intelligent wireless
networks in … the effectiveness and advance of machine learning in intelligent wireless networks. …

… Abstract—In this paper, we study an intelligent reflecting surface (IRS)-aided wireless … the
non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure …