… of mobile network applications, the development of wireless … [11] proposed a hybrid optical
wireless network based on free-… DRL algorithms to wireless networks, providing experimental …

… 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 …

… on deep reinforcement learning for energy optimization and resource allocation in wireless
networks… with ”AND/OR” combinations of them; ”deep reinforcement learning,” ”DRL,” ”energy …

… [14] 2015 The authors in this paper provided an overview of the application of RL based
routing schemes in distributed wireless networks. The challenges, the advantages brought, and …

… deep reinforcement learning approach to wireless networks … We use Google TensorFlow to
implement deep reinforcement … interference alignment wireless networks. Simulation results …

… as enabling techniques for AI-based wireless networks and we focus on delivering a more
applied perspective of MARL to solve wireless communication problems. Table II summarizes …

… However, considering the dynamics and uncertainty that inherently exist in wireless network
environments, conventional approaches for service and resource management that require …

… This work is the first to apply deep reinforcement learning to … learning techniques on various
dynamic wireless network … wireless network can be applied to a larger wireless network. Also…

… [26] present several architectures and core algorithms for deep reinforcement learning,
including deep Q-networks, trust region policy optimization, and asynchronous advantage actor-…

… This work focuses on dynamic channel access optimization in wireless networks, and studies
the dynamic resource optimization problem with deep reinforcement learning algorithms. …